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Taking a new look at ancient books

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At one point in Ovid’s “Tristia,” a series of elegies about the poet’s exile from Rome at the hands of the Emperor Augustus, he deploys an unusual literary device: A book written by Ovid becomes the narrator, representing Ovid’s own hopes for a lasting return to Roman public life.

First the book goes to a “high and shining temple” — Augustus’ own library — but is ordered to leave. The book tries to enter another library and is barred as well. Then it visits a third, without luck.

However, the book reflects, “since a public resting-place is closed to me, may it be granted me to lie hidden in some private spot” — a private book collection, where Ovid’s work might live on. “You too, hands of the people, receive, if you may, our verses dismayed by the shame of their rejection.”

A strong grasp of Ovid’s imaginative flight in the “Tristia” requires both literary analysis and knowledge of the material culture of Roman writing. Indeed, it helps to have the background of MIT literature professor and classicist Stephanie Frampton. Over the last decade, Frampton has become a leading global expert on the interplay between physical forms of writing and the generation of literature and learning in ancient times.

Knowing the history of the Roman book, for starters, can help with understanding the “Tristia.” When Ovid was writing it, around 10 C.E., there were three “public” libraries in Rome — not open to everyone, but places where books could circulate. Those books, incidentally, were usually papyrus scrolls, whose physical existence was tenuous, depending on those scrolls being copied and recopied over time.

Even in less fraught circumstances, then, “authors in antiquity were incredibly sensitive to the fact that their survival was dependent on the maintenance of their works in physical form,” as Frampton writes in her 2019 book “Empire of Letters,” a close study of materiality and writing in the Roman world.

But as Frampton also shows, Ovid’s tale contains a twist, involving his hope, expressed in the poems, that his words might live on as inscriptions in stone. And not only has the “Tristia” survived in book form, but lines from the “Tristia” have been found as inscriptions from modern Bulgaria to the Gulf of Naples. An existing epitaph on a tomb in Rome also draws heavily on Ovid.

“It is in this material record — the visible, physical traces of the ancient world — that perhaps we can come closest to our exiled author, and certainly to his everyday readers,” Frampton writes.

Certainly Frampton’s interest in that material record also underscores the innovative, interdisciplinary nature of her scholarly method. Her work draws upon not only literary analysis and book and manuscript studies, but linguistics, philology, papyrology (the study of papyrus scrolls), and eipgraphy (the study of inscriptions).

Frampton is now working on her next book, with the working title, “Cicero’s Library: The Roman Book and the Making of the Classics.” In this project, which Frampton describes as “the history of classics as seen through book collections,” she explores the advent of the personal library in Rome, as both a status symbol and a collecting practice that helped shaped a new literary canon. (Cicero, who was the first to use the Latin word for “book collection,” is one focal point).   

“The question of the book is, what were the classics of the ancient world and how did they come to be?” Frampton says.

She is currently focused on the new book project during the 2019-20 academic year as the recipient of the Frances A. Yates Long-Term Fellowship at the Warburg Institute in London, a leading center for the study of cultural and intellectual history. For her scholarship and teaching, Frampton was awarded tenure at MIT in 2019.

Frampton’s scholarly interest in studying the classics has many sources, one of which loops back to MIT. Her father, John Frampton, was a mathematician at Northeastern University in Boston who made a midcareer move into linguistics, often working with MIT scholars in the field.

“He was a regular in the linguistics department at MIT through my entire childhood,” Frampton recounts, adding: “Dinner conversation was always: Can you say, ‘Mary the ball did kick?’ or do you have to say, ‘Mary did kick the ball?’ … That was the environment in which I grew up, and that was really important to me.”

That interest in languages prompted Frampton to take Latin while a secondary-school student — her first sustained exposure to ancient writing. At the same time, Frampton recounts, “I was always a reader and always loved literature.” At the University of Chicago, she majored in comparative literature and wrote a senior thesis examining magical realism in the works of Ovid and in Latin American fiction of the 1960s.

Frampton moved to Harvard University’s PhD program in comparative literature, where some of her courses heightened her interest in the material culture of writing.

She earned her PhD in 2011, after completing her dissertation, “Toward a Media History of Writing in Ancient Italy,” which consisted of four discrete studies situating writing in the physical world. Some of this material became integral to “Empire of Letters.” For instance, the poet Lucretius’ “On the Nature of Things,” from around 55 B.C.E., makes the case for atomism, the notion that the world and cosmos were made up from tiny pieces of matter, not four essential elements.

By analogy, Lucretius suggested to readers, atoms were like letters of the alphabet. In Latin the word “elementa” means both atoms and letters, and likely comes from the alphabet’s “L,M,N” sequence. Copying out letters and syllables, Lucretius proposed, helped students understand how both letters and elements could be combined into varying forms. In this and many other historical cases, the substance of ancient thought was virtually embedded in its physical forms.

Frampton joined the MIT faculty in 2012, and has since gained further recognition for her work, including the prestigious Rome Prize, for study at the American Academy in Rome during the 2013-14 academic year. She also received a Loeb Classical Library Foundation fellowship and the Margo Tytus fellowship from the University of Cincinnati in 2013. Additionally, Frampton helped found the Mellon Society of Fellows in Critical Bibliography, housed at the Rare Book School of the University of Virginia, where she has been a senior fellow.

Frampton has also thrown herself into her teaching at MIT, serving as co-chair of the Program in Ancient and Medieval Studies with historian Eric Goldberg. While an introductory Latin class was already offered before she arrived, Frampton introduced an ancient Greek course, and has started teaching advanced classes in both languages, as well as offering courses in classical literatures, translation, media history, and the introductory course to ancient and medieval studies.

“Students [at MIT] are extremely self-directed and motivated by the purest of intellectual interests, which is their own enrichment,” Frampton says.

Frampton has also received support from MIT’s Alumni Class Funds for a project, “Exploring the History and the Future of the Book at MIT,” that aims to develop new learning experiences and resources for students. Part of the program could add new materials and resources that complement MIT’s noted course 21H.343 (Making Books), in which students make books using Renaissance-era methods. Frampton hopes to extend such projects to ancient technologies, so that students might, as a final project, construct, for instance, papyrus scrolls.

It may have taken Ovid’s imagination to think of books as having inner lives, but 2,000 years later, in an MIT classroom, ancient book-making itself may come to life.

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Heilbrunn Timeline of Art History Essays

The origins of writing.

Proto-Cuneiform tablet with seal impressions: administrative account of barley distribution with cylinder seal impression of a male figure, hunting dogs, and boars

Cuneiform tablet: administrative account with entries concerning malt and barley groats

Cylinder seal and modern impression: three "pigtailed ladies" with double-handled vessels

Ira Spar Department of Ancient Near Eastern Art, The Metropolitan Museum of Art

October 2004

The alluvial plains of southern Mesopotamia in the later half of the fourth millennium B.C. witnessed a immense expansion in the number of populated sites. Scholars still debate the reasons for this population increase, which seems too large to be explained simply by normal growth. One site, the city of Uruk , surpassed all others as an urban center surrounded by a group of secondary settlements. It covered approximately 250 hectares, or .96 square miles, and has been called “the first city in world history.” The site was dominated by large temple estates whose need for accounting and disbursing of revenues led to the recording of economic data on clay tablets. The city was ruled by a man depicted in art with many religious functions. He is often called a “ priest-king .” Underneath this office was a stratified society in which certain professions were held in high esteem. One of the earliest written texts from Uruk provides a list of 120 officials including the leader of the city, leader of the law, leader of the plow, and leader of the lambs, as well as specialist terms for priests, metalworkers, potters, and others.

Many other urban sites existed in southern Mesopotamia in close proximity to Uruk. To the east of southern Mesopotamia lay a region located below the Zagros Mountains called by modern scholars Susiana. The name reflects the civilization centered around the site of Susa. There temples were built and clay tablets, dating to about 100 years after the earliest tablets from Uruk, were inscribed with numerals and word-signs. Examples of Uruk-type pottery are found in Susiana as well as in other sites in the Zagros mountain region and in northern and central Iran, attesting to the important influence of Uruk upon writing and material culture. Uruk culture also spread into Syria and southern Turkey, where Uruk-style buildings were constructed in urban settlements.

Recent archaeological research indicates that the origin and spread of writing may be more complex than previously thought. Complex state systems with proto-cuneiform writing on clay and wood may have existed in Syria and Turkey as early as the mid-fourth millennium B.C. If further excavations in these areas confirm this assumption, then writing on clay tablets found at Uruk would constitute only a single phase of the early development of writing. The Uruk archives may reflect a later period when writing “took off” as the need for more permanent accounting practices became evident with the rapid growth of large cities with mixed populations at the end of the fourth millennium B.C. Clay became the preferred medium for recording bureaucratic items as it was abundant, cheap, and durable in comparison to other mediums. Initially, a reed or stick was used to draw pictographs and abstract signs into moistened clay. Some of the earliest pictographs are easily recognizable and decipherable, but most are of an abstract nature and cannot be identified with any known object. Over time, pictographic representation was replaced with wedge-shaped signs, formed by impressing the tip of a reed or wood stylus into the surface of a clay tablet. Modern (nineteenth-century) scholars called this type of writing cuneiform after the Latin term for wedge, cuneus .

Today, about 6,000 proto-cuneiform tablets, with more than 38,000 lines of text, are now known from areas associated with the Uruk culture, while only a few earlier examples are extant. The most popular but not universally accepted theory identifies the Uruk tablets with the Sumerians, a population group that spoke an agglutinative language related to no known linguistic group.

Some of the earliest signs inscribed on the tablets picture rations that needed to be counted, such as grain, fish, and various types of animals. These pictographs could be read in any number of languages much as international road signs can easily be interpreted by drivers from many nations. Personal names, titles of officials, verbal elements, and abstract ideas were difficult to interpret when written with pictorial or abstract signs. A major advance was made when a sign no longer just represented its intended meaning, but also a sound or group of sounds. To use a modern example, a picture of an “eye” could represent both an “eye” and the pronoun “I.” An image of a tin can indicates both an object and the concept “can,” that is, the ability to accomplish a goal. A drawing of a reed can represent both a plant and the verbal element “read.” When taken together, the statement “I can read” can be indicated by picture writing in which each picture represents a sound or another word different from an object with the same or similar sound.

This new way of interpreting signs is called the rebus principle. Only a few examples of its use exist in the earliest stages of cuneiform from between 3200 and 3000 B.C. The consistent use of this type of phonetic writing only becomes apparent after 2600 B.C. It constitutes the beginning of a true writing system characterized by a complex combination of word-signs and phonograms—signs for vowels and syllables—that allowed the scribe to express ideas. By the middle of the third millennium B.C. , cuneiform primarily written on clay tablets was used for a vast array of economic, religious, political, literary, and scholarly documents.

Spar, Ira. “The Origins of Writing.” In Heilbrunn Timeline of Art History . New York: The Metropolitan Museum of Art, 2000–. http://www.metmuseum.org/toah/hd/wrtg/hd_wrtg.htm (October 2004)

Further Reading

Glassner, Jean-Jacques. The Invention of Cuneiform Writing in Sumer . Baltimore: Johns Hopkins University Press, 2003.

Houston, Stephen D. The First Writing: Script Invention as History and Process . Cambridge: Cambridge University Press, 2004.

Nissen, Hans J. "The Archaic Texts from Uruk." World Archaeology 17 (1986), pp. 317–34. n/a: n/a, n/a.

Nissen, Hans J., Peter Damerow, and Robert K. Englund. Archaic Bookkeeping: Early Writing and Techniques of Economic Administration in the Ancient Near East . Chicago: University of Chicago Press, 1993.

Walker, C. B. F. Cuneiform . Berkeley: University of California Press, 1987.

Additional Essays by Ira Spar

• Spar, Ira. “ Mesopotamian Creation Myths .” (April 2009)
• Spar, Ira. “ Flood Stories .” (April 2009)
• Spar, Ira. “ Gilgamesh .” (April 2009)
• Spar, Ira. “ Mesopotamian Deities .” (April 2009)
• Spar, Ira. “ The Gods and Goddesses of Canaan .” (April 2009)

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• Assyria, 1365–609 B.C.
• Early Dynastic Sculpture, 2900–2350 B.C.
• Early Excavations in Assyria
• Etruscan Language and Inscriptions
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List of Rulers

• List of Rulers of Mesopotamia
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• Open access
• Published: 09 March 2022

Restoring and attributing ancient texts using deep neural networks

• Yannis Assael   ORCID: orcid.org/0000-0001-7408-3847 1   na1 ,
• Thea Sommerschield   ORCID: orcid.org/0000-0002-6965-8105 2 , 3   na1 ,
• Brendan Shillingford 1 ,
• Mahyar Bordbar 1 ,
• John Pavlopoulos   ORCID: orcid.org/0000-0001-9188-7425 4 ,
• Marita Chatzipanagiotou 4 ,
• Ion Androutsopoulos 4 ,
• Jonathan Prag 5 &
• Nando de Freitas 1  

Nature volume  603 ,  pages 280–283 ( 2022 ) Cite this article

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• Archaeology
• Computer science

Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations 1 . However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

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Epigraphy is the study of texts—inscriptions—written directly on durable materials (stone, pottery, metal) by individuals, groups and institutions of the ancient world 2 , 3 . Thousands of inscriptions have survived to our time, but many have been damaged over the centuries and their texts are now fragmentary. Inscriptions may also be moved or trafficked far from their original location 4 , and radiocarbon dating is unusable owing to the inorganic nature of most inscribed supports. Specialist epigraphers must then reconstruct the missing text, a process known as text restoration (Fig. 1 ), and establish the original place and date of writing, tasks known as geographical attribution and chronological attribution, respectively 5 . These three tasks are crucial steps towards placing an inscription both in history and within the world of the people who wrote and read it 6 , 7 . However, these tasks are non-trivial, and traditional methods in epigraphy involve highly complex, time-consuming and specialized workflows.

This inscription ( Inscriptiones Graecae , volume 1, edition 3, document 4, face B ( IG I 3 4B)) records a decree concerning the Acropolis of Athens and dates to 485/4 bc . Marsyas, Epigraphic Museum, WikiMedia CC BY 2.5.

When restoring damaged inscriptions, epigraphers rely on accessing vast repositories of information to find textual and contextual parallels 8 . These repositories primarily consist of a researcher’s mnemonic repertoire of parallels and, more recently, of digital corpora for performing ‘string matching’ searches. However, differences in the search query can exclude or obfuscate relevant results, and it is almost impossible to estimate the true probability distribution of possible restorations. Attributing an inscription is equally problematic—if it was moved, or if useful internal dating elements are missing, historians must find alternative criteria to attribute the place and date of writing (such as letterforms, dialects) 9 . Inevitably, a high level of generalization is often involved (chronological attribution intervals can be very long).

Deep learning for epigraphy

Here we overcome the constraints of current epigraphic methods by using state-of-the-art machine learning research. Inspired by biological neural networks, deep neural networks can discover and harness intricate statistical patterns in vast quantities of data 10 . Recent increases in computational power have enabled these models to tackle challenges of growing sophistication in many fields 11 , 12 , 13 , 14 , including the study of ancient languages 15 , 16 , 17 , 18 .

We present Ithaca, a deep neural network architecture trained to simultaneously perform the tasks of textual restoration, geographical attribution and chronological attribution. Ithaca, which was named after the Greek island that eluded the hero Odysseus’ homecoming, was trained on inscriptions written in the ancient Greek language and across the ancient Mediterranean world between the seventh century bc and the fifth century ad . This choice was due to two main reasons. First, the variability of contents and context of the Greek epigraphic record, which makes it an excellent challenge for language processing; and second, the availability of digitized corpora for ancient Greek, an essential resource for training machine learning models.

Working with Greek inscriptions

To train Ithaca, we developed a pipeline to retrieve the unprocessed Packard Humanities Institute (PHI) 19 , 20 dataset, which consists of the transcribed texts of 178,551 inscriptions. This process required rendering the text machine-actionable, normalizing epigraphic notations, reducing noise and efficiently handling all irregularities. Each PHI inscription is assigned a unique numerical ID, and is labelled with metadata relating to the place and time of writing. PHI lists a total of 84 ancient regions; whereas the chronological information is noted in a wide variety of formats, varying from historical eras to precise year intervals, written in several languages, lacking in standardized notation and often using fuzzy wording 21 . After crafting an extended ruleset to process and filter the data (Methods), the resulting dataset I.PHI is to our knowledge the largest multitask dataset of machine-actionable epigraphical text, containing 78,608 inscriptions.

Ithaca is a model for epigraphic tasks

The architecture of Ithaca was carefully tailored to each of the three epigraphic tasks, meaningfully handling long-term context information and producing interpretable outputs to enhance the potential for human–machine cooperation. To begin, contextual information is captured more comprehensively by representing the inputs as words; however, parts of words could have been lost over the centuries. To address this challenge, we process the input text as character and word representations jointly, representing damaged, missing or unknown words with a special symbol ‘[unk]’.

Next, to enable large-scale processing, Ithaca’s torso is based on a neural network architecture called the transformer 22 , which uses an attention mechanism to weigh the influence of different parts of the input (such as characters, words) on the model’s decision-making process. The attention mechanism is informed of the position of each part of the input text by concatenating the input character and word representations with their sequential positional information. Ithaca’s torso consists of stacked transformer blocks: each block outputs a sequence of processed representations of which the length is equal to the number of input characters, and the output of each block becomes the input of the next. The final output of the torso is passed to three different task heads that handle restoration, geographical attribution and chronological attribution, respectively. Each head consists of a shallow feedforward neural network, specifically trained for each task. In the example shown in Fig. 2 , the restoration head predicts the three missing characters; the geographical attribution head classifies the inscription among 84 regions; and the chronological attribution head dates it to between 800 bc and ad  800.

The first three characters of the phrase were hidden and their restoration is proposed. In tandem, Ithaca also predicts the inscription’s region and date.

Interpreting the outputs

Our intention was to maximize the collaborative potential between historians and deep learning. Ithaca’s architecture was therefore designed to provide intelligible outputs, while featuring multiple visualization methods to augment the interpretability of the model’s predictive hypotheses. For the task of restoration, instead of providing historians with a single restoration hypothesis, Ithaca offers a set of the top 20 decoded predictions ranked by probability (Fig. 3a ). This first visualization facilitates the pairing of Ithaca’s suggestions with historians’ contextual knowledge, therefore assisting human decision-making. This is complemented by saliency maps, a method used to identify which unique input features contributed the most to the model’s predictions, for both the restoration and attribution tasks (Fig. 3d and Extended Data Fig. 5a ).

a , Restoration predictions for six missing characters (dashes) in an Athenian inscription ( IG II² 116). The top restoration, in green, is correct (συμμαχία, ‘alliance’). Note how the following hypotheses (ἐκκλησία, ‘assembly’; and προξενία, ‘treaty between state and foreigner’), highlighted in red, typically occur in Athenian political decrees 23 , revealing Ithaca’s receptivity to context. b , Geographical attribution of an inscription from Amorgos ( IG XII 7, 2). Ithaca’s top prediction is correct, and the closest predictions are neighbouring regions. c , Date distribution for an inscription from Delos ( IG XI 4, 579). The ground-truth date interval 300–250 bc is shown in grey; Ithaca’s predicted distribution is shown in yellow and has a mean at 273 bc (green). Ithaca’s predictions show a higher confidence for the interval’s higher date margin, therefore potentially narrowing the broad ground-truth dating bracket. d , Chronological attribution saliency map for an Athenian inscription ( IG I³ 371). The colour intensity illustrates the importance of each input. Ithaca focuses on the personal name (Νικίας, ‘Nikias’) and the Greek commanders’ rank (στρατεγοίς, ‘generals’). Nikias had a key role in the great Athenian expedition to Sicily 24 , 25 , 26 , the historical event to which this very inscription pertains. Ithaca dates the inscription to 413 bc , matching the exact range proposed by historians (414–413 bc ).

For the geographical attribution task, Ithaca classifies the input text among 84 regions, and the ranked list of possible region predictions is visually implemented with both a map and a bar chart (Fig. 3b ). Finally, to expand interpretability for the chronological attribution task, instead of outputting a single date value, we predict a categorical distribution over dates (Fig. 3c ). By so doing, Ithaca can handle ground-truth labels more effectively, as the labels correspond to date intervals. More precisely, Ithaca discretizes all dates between 800 bc and ad 800 into 10-year bins, resulting in 160 decades. For example, the date range 300–250 bc is represented as 5 decades of equal 20% probability, whereas an inscription dated to 305 bc would be assigned to the single-decade-bin 300–310 bc with 100% probability.

Experimental evaluation

To compare performance in the three epigraphic tasks, we use four methods. First, we evaluate the difficulty of the restoration task by assigning two evaluators with epigraphical expertise (‘ancient historian’) a set of damaged inscriptions to restore, using the training set to search for textual parallels. Second, we provide the human experts with a ranked list of Ithaca’s top 20 restoration hypotheses to inform their predictions (‘ancient historian and Ithaca’), therefore assessing the true impact of our work as a cooperative research aid. Third, as a computational baseline we reimplement our previous work Pythia 15 —a sequence-to-sequence recurrent neural network for the task of ancient-text restoration. Finally, for the attribution tasks, we introduce an ablation of the epigrapher’s workflow, the ‘onomastics’ baseline: annotators were tasked with attributing a set of texts, exclusively using the known distribution of Greek personal names across time and space to infer geographical and chronological indicia 27 .

We introduce the following metrics to measure each method’s performance. For restoration, to obviate the lack of ground truths in damaged inscriptions, we artificially hide 1 to 10 characters of undamaged input text and treat the original sequences as the target. The first metric used is the character error rate (CER), which counts the normalized differences between the top predicted restoration sequence and the target sequence. Furthermore, we use top- k accuracy to measure whether the correct restoration or region label for geographical attribution is among the top k predictions, therefore quantifying Ithaca’s potential as an assistive tool. For chronological attribution, we use a distance metric (Methods) to measure the distance in years from the predictive distribution’s mean and the ground-truth interval, the latter being defined by a minimum and a maximum date.

As shown in Table 1 , for the task of restoration, Ithaca consistently outperforms the competing methods, scoring a 26.3% CER and 61.8% top 1 accuracy. Specifically, our model achieves a 2.2× lower (that is, better) CER compared with human experts, whereas Ithaca’s top 20 predictions achieve a 1.5× improved performance compared with Pythia, with an accuracy of 78.3%. Notably, when pairing historians with Ithaca (ancient historian and Ithaca), human experts achieve an 18.3% CER and 71.7% top 1 accuracy, therefore demonstrating a considerable 3.2× and 2.8× improvement compared with their original CER and top 1 scores. Regarding the attribution to regions, Ithaca has 70.8% top 1 and 82.1% top 3 predictive accuracy. Finally, for chronological attribution, whereas the onomastics human baseline predictions are within an average of 144.4 and median of 94.5 years from the ground-truth date intervals, Ithaca’s predictions, based on the totality of texts, have an average distance of 29.3 years from the target dating brackets, with a median distance of only 3 years.

Contributing to historical debates

Our experimental evaluation effectively demonstrates Ithaca’s impact on the study of inscriptions, and their consequent value as historical evidence. First, Ithaca can discover epigraphic patterns on an unprecedented scale and in unparalleled detail, harnessing substantial quantities of epigraphic data (I.PHI) to achieve the high performance observed in all three epigraphic tasks. Moreover, whereas Ithaca may have outperformed historians in the first baseline, the combination of a historian’s own (contextual) knowledge alongside Ithaca’s assistive input resulted in an even greater improvement over the model’s performance. This collaborative potential is augmented by Ithaca’s design decisions, and by the different visualization aids increasing the interpretability of outputs, therefore enabling historians to evaluate multiple hypotheses. As a consequence, Ithaca could help historians narrow the wide or vague date brackets they are sometimes forced to resort to, by helping increase precision and establish relative datings for historical events, and even contributing to current methodological debates in ancient history.

Indeed, to demonstrate Ithaca’s creative potential, we applied our model to a contemporary dispute concerning the dating of a group of inscriptions whose interpretation is central to the political history of classical Athens. Historians disagree on whether these decrees should pre- or post-date 446/5 bc depending on the (dis)belief in using specific letterforms as dating criteria (the three-bar sigma dating convention) 28 . In recent years, the validity of this dating convention was called into question 29 —the dates of many decrees have been pushed to the 420s bc , therefore profoundly influencing our understanding of Athenian imperialism 30 .

This group of disputed Athenian decrees exists in our dataset: their dating labels follow the conventional ‘higher’ dates (pre-446/5 bc ). We excluded these texts from the dataset and trained Ithaca on all of the remaining inscriptions. Notably, Ithaca’s predictions for these held-out texts independently align with the most recent dating breakthroughs, therefore overturning the conventional historical reading based on the sigma dating criterion. More specifically, whereas the I.PHI labels are on average 27 years off the ‘lower’ dating proposed by modern re-evaluations, Ithaca’s predictions are on average only 5 years off the newly proposed ground truths.

This example eloquently illustrates how models such as Ithaca can contribute to key methodological debates on the chronological reorganization of Athenian imperialism, one of the most important moments in Greek history. In no instance do Ithaca’s predictions for this group of inscriptions exceed 433 bc : Ithaca’s average predicted date for all of these decrees is 421 bc . Historians may now use Ithaca’s interpretability-augmenting aids (such as saliency maps) to examine these predictions further and bring more clarity to Athenian history.

Conclusions

Ithaca is to our knowledge the first epigraphic restoration and attribution model of its kind. By substantially improving the accuracy and speed of the epigrapher’s pipeline, it may assist the restoration and attribution of newly discovered or uncertain inscriptions, transforming their value as historical sources and helping historians to achieve a more holistic understanding of the distribution and nature of epigraphic habits across the ancient world. To achieve this goal, our interdisciplinary team created an open-source and publicly available interface ( https://ithaca.deepmind.com ), enabling historians to use Ithaca for their personal research, while facilitating its development for further applications.

In fact, the methods introduced in this research apply to all disciplines dealing with ancient text (papyrology, numismatics, codicology), to any language (ancient or modern), also integrating additional metadata (inscription images, stylometrics). Furthermore, Ithaca’s quintessentially interactive nature as a cooperative research aid lends itself as an effective set-up for future machine learning research by adding humans into the training loop.

In conclusion, the transformational impact of this work lies in delivering state-of-the-art research aids that extend the scope of ancient history and the humanities.

Previous work

In recent years, several works have proposed traditional machine learning approaches to the study of ancient texts. This body of work has focused on optical character recognition and visual analysis 31 , 32 , 33 , 34 , writer identification 35 , 36 , 37 and text analysis 38 , 39 , 40 , 41 , 42 , 43 , 44 , stylometrics 45 and document dating 46 . It is only very recently that scholarship has begun to use deep learning and neural networks for optical character recognition 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , text analysis 56 , machine translation of ancient texts 57 , 58 , 59 , authorship attribution 60 , 61 and deciphering ancient languages 62 , 63 , and been applied to study the form and style of epigraphic monuments 64 .

The closest work to Ithaca is our 2019 research on ancient text restoration: Pythia 15 . Pythia was to our knowledge the first ancient text restoration model to use deep neural networks, and was followed by blank language models 18 , Babylonian 65 and Korean text translation and restoration 17 , Latin BERT for language modelling, part-of-speech tagging, word sense disambiguation and word similarity 16 , and the classification of Cuneiform tablets by period 66 .

Ithaca is to our knowledge the first model to tackle the three central tasks in the epigrapher’s workflow holistically. Not only does it advance the previous state-of-the-art set by Pythia, but it also uses deep learning for geographical and chronological attribution for the very first time and on an unprecedented scale. Ithaca offers interpretable outputs, showcasing the rising importance of cooperation between human experts and machine learning 67 —as exemplified by our experimental evaluation.

Most importantly, this work shows how matching human experts with deep learning architectures to tackle tasks collaboratively can surpass the individual (unaided) performance of both humans and model on the same tasks. Indeed, recent medical research 68 , 69 further confirms the importance of hybrid architectures in addressing real-world problems. The present work makes human expert interaction possible by visualizing the output probability distributions for all tasks using multiple charts and maps, and augmenting their interpretability by means of saliency maps. It is our hope that this work may set a new standard for the field of digital epigraphy, by using advanced deep learning architectures to support the work of ancient historians.

Generating the I.PHI corpus

When restoring damaged inscriptions, epigraphers conjecture the total number of missing characters based on grammatical and syntactical considerations, and on the reconstructed physical form of the text 5 . Conjectured missing characters that cannot be restored are conventionally marked with periods or hyphens, one hyphen equating to one missing character. Moreover, PHI presents interpretive transcriptions of the texts (including capitalization, punctuation, word division, lower-case letter conversion).

Thus, moving from the PHI dataset, we substantially expand the ruleset for filtering human annotations previously conceived for Pythia, rendering the text machine-actionable. We removed 9,441 duplicate texts and filtered out all inscriptions under 50 characters in length, whereas, in Pythia’s dataset, we had excluded all texts with fewer than 100 characters. To increase the amount of available text, we retained the supplements proposed by epigraphers (conventionally added between square brackets), and we matched the number of unrestored characters with an equal number of ‘–’ symbols, as is commonly done by epigraphers (Extended Data Fig. 1 ).

Each PHI inscription is assigned to a region of the ancient Mediterranean world (Extended Data Fig. 2 ), and includes an additional metadata string referring to the date proposed by epigraphers for the text (Extended Data Fig. 1 ). The chronological information is noted in a variety of formats (historical eras, precise year intervals); in several languages (including Latin); ranging before ( bce ) and after ( ce ) the Common Era; lacking in standardized notation (‘early’, ‘first half’, ‘1st half’, ‘beginning’, ‘beg.’) and often using fuzzy wording (‘late 7th/6th ac.’, ‘ca. 100 a.?’, ‘bef. 64 ad ’). After crafting an extended ruleset, we succeeded in generating well-defined date intervals for 60% of all PHI inscriptions, as the chronological metadata of the remaining 40% is either missing or unprocessable. The resulting I.PHI dataset contains 1.93× more inscriptions than the previous Pythia’s dataset. The texts of which the numerical PHI identifier (PHI ID) ended in 3 or 4 were held out and used as test and validation sets, respectively (Extended Data Table 1 ).

Ithaca architecture

For each inscription, the input of the model consists of (1) a sequence of character embeddings (real-valued vectors, each representing the character of the alphabet that occurs at the corresponding position of the inscription); (2) an equally long sequence of word embeddings (real-valued vectors, each representing the vocabulary word at the corresponding character position of the inscription; Fig. 2 ); and (3) positional embeddings (also real-valued vectors, each representing a position of the input sequence). The first two kinds of embeddings are randomly initialized and learned when training Ithaca (via backpropagation). The positional embeddings are also trainable and they are initialized with a separate sinusoidal function per dimension 22 to maintain a symmetrical distance between neighbouring steps and smoothly decay over the maximum length of 768 characters. Our vocabulary includes every word appearing more than 10 times in I.PHI (35,884 words), while damaged or ‘unknown’ (under-represented) words are rendered with an ‘[unk]’ symbol. The joint use of character and word embeddings enables the architecture of Ithaca to be both character- and context-aware 70 , 71 , 72 . Finally, the input sequence is padded with a start-of-sentence character ‘<’.

The three input sequences are combined by concatenating the different embeddings per-character position and the resulting sequence is fed through the torso of the model. The architecture of Ithaca’s torso consists of eight stacked transformer decoder blocks, inspired by the large-scale transformer model BigBird 73 . Every block uses four sparse attention heads (using global, local and random attention mechanisms), which reduce the context-length dependency from quadratic to linear, therefore enabling the model to handle lengthier sequences 73 compared with classical transformers. Furthermore, the attention mechanism is ‘multi-head’ (Fig. 2 ) in the sense that it can learn to consider different types of information extracted from the input. For example, different attention heads may be sensitive to particular character sequences, or more perceptive to certain words and phrases with distinctive morphosyntactic or semantic features. Finally, to overcome problems that hinder the stacking of such complicated blocks, each transformer block uses residual connections and layer normalization (shown as ‘add and normalize’ in Fig. 2 ).

Ithaca’s torso outputs a sequence whose length is equal to the number of input characters, and each item in this sequence is a 2,048-dimensional embedding vector. Each task head consists of a two-layer feedforward network followed by a softmax function. There are three different task heads, handling region attribution, chronological attribution and restoration respectively. To predict the regions and dates, Ithaca uses the first output embedding ( t  = 1) and passes it on to the two corresponding heads. This arrangement is similar to that of DocBERT 74 and works better than other pooling methods (such as mean- and max-pooling over the output embeddings) in our experimental evaluation. Finally, for the restoration task, Ithaca uses the remaining output embeddings ( t  > 1) as there is a direct correspondence with the input text characters: for each missing character position, the corresponding output embedding of the torso is fed to the head of the restoration task, which predicts the missing character.

Data preparation and augmentation

I.PHI may be the first multitask dataset of machine-actionable epigraphical text, but its size is still several orders of magnitude smaller than modern typical language datasets. To avert the risk of overfitting, which is common in large-scale deep neural network architectures, we apply several data augmentation methods, described below, to artificially increase the size of I.PHI’s training set. Our preliminary experimental evaluation found that these methods are crucial in achieving the reported performance. These augmentation methods are applied anew whenever a training inscription is re-encountered in each training epoch.

Text clipping

For each inscription, we select an arbitrary section of its text and ignore the remaining text. We implement this by first sampling a segment length between 50 and 768 characters, and then sampling the starting index of the segment. This method helps Ithaca to generalize and improve the handling of partial inputs.

Text masking

Forcing the model to rely on contextual information often leads to improvements in prediction. To achieve this in our model, during training, we randomly hide up to half of the input text by replacing sequences of characters sampled from a geometric distribution ( P  = 0.1) with ‘–’. This span masking is intended to replicate the distribution over the length of missing characters estimated from the dataset, and uses the hidden ground-truth characters as target labels for the restoration task.

Word deletion

During training, we also delete words from each input text (without replacing them with any special characters in this case) with a 20% probability. Here, the goal is again to increase variability in the training data to improve the model’s ability to generalize over all possible ways in which inscriptions are damaged 75 .

Sentence swap

By randomly swapping sentences in the input text with a 25% probability, we generate multiple input–label pairs for the auxiliary task of next-sentence prediction (NSP) 75 (see below).

Data circularity

Ithaca’s source dataset (PHI) is a synthesis of generations of scholarly research. Epigraphers typically restore texts and attribute them chronologically through a process of induction. Textual restorations are proposed on the basis of parallels, mediated by wider historical and linguistic knowledge; chronological attributions are proposed partly from archaeological and contextual information, partly from textual form and content, and partly from textual and material parallels. The texts on which Ithaca trains include previous scholarly restorations; and the dates recorded are the product of accumulated scholarly knowledge and induction from archaeological, historical and textual study. This might be thought to imply circularity, but that would be true only if Ithaca were operating in a world of objective data and aiming to offer a single objectively true solution. Rather, Ithaca is an assistive tool aiming to improve on and facilitate a scholarly process of induction, model uncertainty and propose possible solutions for the scholar to consider.

Considering textual restoration, Ithaca avoids the risk of ‘history from square brackets’ 76 , 77 , 78 (assuming any proposed restoration to be ground truth, meaning the accepted consensus, rather than merely one of several hypotheses), because none of Ithaca’s proposed restorations are assumed to be objectively certain—instead, they are presented as plausible suggestions. Furthermore, the inclusion of existing scholarly conjectures within the training set itself does not constitute a form of ‘history from square brackets’, as such conjectures are themselves plausible restorations achieved by a process of induction and considered acceptable by one or more experts, and as such are precisely the sort of result that Ithaca itself aims to generate. The value of Ithaca is indeed its ability to learn from the largest possible dataset of attested and possible texts, making the underlying process of inductive reasoning as powerful as possible, and so generating possible restorations for scholars to evaluate.

As for chronological attribution, the dataset on which Ithaca trains is founded in the past study of multiple elements (such as archaeological provenance, material form, textual content and form). Ithaca in turn learns through close attention to the text alone. The attributions proposed by Ithaca therefore have their basis in the inductive study of a vast textual dataset and its correlation to chronological data that are more broadly derived. Ithaca is therefore able to bring some refinement to those attempts to date the texts through the application of machine learning specifically to the textual patterns in that data. Thus, Ithaca is, in this case, a part of that scholarly process, and no more or less circular in its reasoning than any other scholar.

Training on epigraphic tasks

For the task of restoration, we use the text-masking augmentation method to mask parts of the input and produce ground truths. We subsequently use a cross-entropy loss to train Ithaca to predict the missing characters. The cross-entropy loss is also used for geographical attribution, using the region metadata as target labels. We further apply label smoothing with a coefficient of 10% to avoid overfitting and to provide historians with a smoother distribution of predicted hypotheses. For the task of chronological attribution, Ithaca discretizes all dates between 800 bc and ad 800 with a bin size of 10 years. This range covers the majority of the PHI dataset entries and encompasses the conventional date range for Greek epigraphy. The processed ground-truth date intervals are discretized into bins of equal probability, forming the target probability distribution. The limitations of discretizing and amalgamating date ranges of different levels of precision based on past scholarship have been noted 79 , 80 —the scale of data on which Ithaca trains, together with the increased attention to textual patterns (compared with the previous paragraph), at least partially meet that challenge. We then use the Kullback–Leibler divergence to minimize the difference between target and predicted probability distribution (Fig. 3c ).

Finally, to allow for better modelling of context, we introduce a next sentence prediction loss, an auxiliary function common to language modelling tasks 81 . During training, we randomly shuffle some of the sentences of the input text, and at the end of each (non-final) sentence (marked by a full stop, ʻ.ʼ) we predict whether the next sentence is in the correct order (valid) or a product of the shuffling augmentation. By deploying the torso’s output embeddings for the full stops, we introduce an additional feedforward network that uses binary cross-entropy to predict the validity of the next sentence whenever a ʻ.ʼ character appears.

Using this setup, Ithaca was trained for a week on 128 Tensor Processing Units (TPU) v4 pods on the Google Cloud Platform. The effective batch size was 8,192 texts and a LAMB optimizer 82 was used to optimize Ithaca’s parameters with a learning rate of 3 × 10 −4 . Using Bayesian optimization hyperparameter search, the loss functions of each task were combined using the following function:

We do not use a separate masked (token) language modelling loss, which is commonly used when pretraining language models, as it is very similar to the restoration loss, although the latter masks characters instead of tokens.

To obtain Ithaca’s textual restoration predictions, we select a sequence of missing characters to predict and use Beam Search with a beam width of 100. Instead of using a standard sequential Beam Search, we take advantage of Ithaca’s non-autoregressive nature 83 , 84 , 85 , and use a non-sequential one instead. Each beam starts with the prediction scoring the highest confidence 86 , then proceeds iteratively to restore at each time-step the characters of which the certainty is the highest. We found that this version of Beam Search performed substantially better in our evaluation metrics. For region attribution, the outputs are presented as a plot of the top 10 predictions; for chronological attributions, we visualize the model’s predictive distribution over possible date bins. Finally, to reduce the variance of random segment selections, we repeat the process ten times and report results averaged over the iterations.

Ancient historian baseline

The evaluators for ancient text restoration were two graduate students of ancient history, with 7 years of historical and linguistic training and specializing in Greek history and epigraphic documents. Thus, they can be assumed to be more capable than the ‘average’ ancient historian, but not yet equivalent to (the very small number) of established specialists in the field. The scholars were allowed to use the training set to search for textual ‘parallels’, and made an average of 50 restorations in 2 h.

Although Ithaca can indeed propose restoration hypotheses faster, and model its prediction uncertainty, it cannot make choices on the basis of historical and material context. Thus, the experimental setup cannot be considered to be direct comparison between human historians and machine learning, nor are the evaluators assumed to be a proxy for all historians. Instead, the experiment was intended to measure the difficulty of the task and the potential for cooperative artificial intelligence.

Onomastics baseline

Greek nomenclature is commonly used by epigraphers as one of several elements to inform their attribution predictions 87 . Inspired by this method in the wider epigraphic workflow, we designed an ‘onomastic’ baseline, of which the predictions are based exclusively on the metadata associated with Greek personal names. Five annotators searched for name(s) appearing in a set of inscriptions in the Lexicon of Greek Personal Names (LGPN), a database recording the geographical and chronological distribution of ancient names 27 , and based their attribution hypotheses on the LGPN’s distribution data. Evaluators were also provided with the inscription’s date or place of writing for the geographical or chronological attribution tasks, respectively.

Restoration metrics

To evaluate different restoration methods, for every inscription, we predict a sequence of 1–10 contiguous missing characters. These lengths account for 83% of the distribution of missing character lengths in I.PHI, and enable comparisons with both previous work and the human baselines. Note that, thanks to the text-masking augmentation adopted during training, Ithaca could potentially restore up to half of the input text.

Although the number of characters to be predicted reflects the difficulty of the task, the restored sequences in the test sets held out for human evaluation might not necessarily maintain the same distribution of lengths (as they were a subset of the test set). Thus, instead of reporting only the average scores over the entire test set (as done in previous work), we chose to account for these length discrepancies and compute the average scores for each restored sequence length. First, we computed a separate CER for all samples of each length (between 1–10 characters),

where I is the indicator function, len i denotes the length of the i -th sample, N is the number of samples, pred i is the predicted sequence of missing characters of the i -th sample and target i the corresponding target sequence. We next calculate the average for all lengths:

where L  = 10 is the maximum length.

As human annotators annotated only a subset of the test set owing to time constraints, macro-averaging assigns equal importance to all sample lengths to represent the difficulty of the task independently of dataset statistics, and therefore enabling a fair comparison of the methods. Similarly, for accuracy, we first computed a separate accuracy per length, and then the average:

Chronological attribution metric

As our model outputs a predictive distribution in the chronological attribution task, we introduce an interpretable metric to measure the distance in years between a prediction and the ground-truth interval (Fig. 3c ). More specifically, we use a distance metric between the mean of the predictive distribution and the target ground-truth interval; the latter is defined by a minimum (gt min ) and a maximum (gt max ) date in years:

Model selection

The final model was obtained by storing the best-performing model on the validation set by using a combined metric that sums the accuracy for textual restoration and geographical attribution, and the distance in years divided by 100 for chronological attribution to make the magnitude comparable. The extensive computational resources required to train our model made the Pareto frontier computation infeasible.

Chronological attribution results

Ithaca’s predictions are 5× closer to ground truths than those recorded in the onomastics baseline (144.4 years). More specifically, Ithaca’s average date prediction is within 28.7 years of the ground-truth date interval, and the median is only 3 years. The results are shown in detail in Extended Data Fig. 3 .

Restoring full texts with Ithaca

To overcome memory constraints and length limitations for long inscriptions (>768 characters), Ithaca can be applied iteratively to restore all missing text in a damaged inscription. We experimented with this option on inscription IG II² 116, which is missing 378 characters, and compared Ithaca’s predictions with those of our previous work Pythia on the same text, using the authoritative edition published by Rhodes and Osborne as ground truths 88 . The models’ correct restorations are highlighted in green (Extended Data Fig. 4 ), and the erroneous ones in red. In a real-world scenario, both Ithaca and Pythia would provide a ranked set of 20 restoration hypotheses. The comparison in performance between Pythia and Ithaca is stark (74 versus 45 mistakes): moreover, in all cases in which the restoration is in red, the ground-truth sequence existed within the beam of Ithaca’s top 20 hypotheses.

Geographical attribution of Delphic inscriptions

Epigraphers determine the original location where an inscription was written by examining the personal names, local or regional dialectal varieties, and idiosyncratic lexicon or style of an inscription. Moving from this methodological premise, and to discover underlying patterns in Ithaca’s geographical predictions, we compute statistics to track the words that appear most frequently in texts whose region Ithaca predicts correctly. Thus, for each word of the test set, we compute an average accuracy and a frequency of appearance. This visualization is intended to evaluate whether the occurrence of particular words could be correlated to the model’s geographical attributions.

The most frequent words that appear in texts with high prediction accuracy clustered primarily in inscriptions from the region of Delphi, and pertained to the epigraphic genre of ‘manumission inscriptions’ (Extended Data Table 2 for an example). Ancient Greek society depended heavily on unfree labour, but slaves could be freed through a process known as ‘manumission’, which was publicly documented and certified by inscriptions 89 , 90 . Over 1,000 such texts dating between around 201 bc and ad 100 have been found in Delphi 91 , 92 . The words appearing in Ithaca’s accuracy statistics are identified as typical of these manumission texts, which are in turn distinctive of this region (for example, ἐπίστευσε, άποδμενος, καταδουλισμωι, βεβαιωτήρ, ωνάν): these words could therefore be underpinning the correct attribution predictions (a detailed example is offered in Extended Data Table 2 ). Further study can now be dedicated to investigating stylized manumissions as distinctive of Delphi.

To further assess the impact of Ithaca’s output visualization techniques in a real-world scenario, we also analysed the saliency maps for geographical attribution of the manumission inscriptions. Indeed, the saliency maps for the Delphic inscription BCH 66/67 (1942/3) 82,9, for example, highlight words typically found in manumission texts and which also appear in Ithaca’s word statistics: these words (ἐπίστευσε, ἐλευθερος, ποιέουσα, ἀποτρέχουσα) have the most important role in the geographical attribution of the inscription, while also betraying the text’s genre as a typical slave manumission inscription (Extended Data Fig. 5b ).

Redating disputed Athenian decrees

In the absence of helpful internal evidence of a text’s date (for example, the mention of known historical figures 93 ), epigraphers typically derive an approximate date on the basis of a text’s content, letterforms and grammatical criteria. For example, one of the most notorious methodological debates in epigraphy concerns the ‘three-bar sigma’ dating convention, which holds that no Athenian public document containing the three-bar sigma letter (ϟ) could be dated after the year 446/5 bc , when the letter was supplanted by the four-bar sigma (Σ). On the basis of this chronological benchmark, a group of inscriptions whose interpretation is central to the political history of Classical Athens, and which feature the earlier letter ϟ, were dated to pre-446/5 bc by many authoritative corpora 28 , 94 . This set of decrees exists in the PHI dataset (Extended Data Table 3 ), and their dating labels follow the conventional ‘higher’ dating of the three-bar sigma criterion.

However, this orthodox dating system soon proved to be problematic: the high dates proposed for these decrees did not agree with contemporary literary accounts reporting on Athenian imperialist policies. Few historians contested the validity of the sigma criterion 29 , 95 , but in 1990 photo-enhancement and laser scanning confirmed the down-dating of an inscription featuring the three-bar sigma (the Egesta decree, IG I 3 11) from 458 to 418 bc 96 . Over the following decade, the sigma’s traditional cut-off date was revisited, and the dates of other decrees were also pushed back 28 , 97 .

Ithaca’s predictions for this set of disputed inscriptions independently align with the most recent dating breakthroughs (Extended Data Fig. 6 ). For example, the (in)famous Chalcis decree ( IG I 3 40; Extended Data Fig. 7 ), which records an oath of allegiance sworn by the city of Chalcis to Athens 98 and traditionally dated to 446/5 bc 28 , is attributed by Ithaca to 420 bc , therefore concurring with the lower dating hypothesis of 424/3 bc proposed by more recent scholarship 99 . Perhaps the most compelling example of Ithaca’s prediction independently aligning with a lower dating hypothesis is the decree of Kleinias ( IG I 3 34) 100 , regulating the collection of tribute across the Athenian empire. The sigma dating system would assign the inscription to 448/7 bc 28 , but scholars have recently challenged this orthodoxy and proposed the earlier date of 425/4 bc 101 . Ithaca’s prediction agrees precisely with the latter, dating the famous decree to 424 bc .

Ithaca has re-dated a number of these key inscriptions with striking accuracy (Extended Data Table 3 ). Although it may seem slight, this 40/30-year chronological reorganization has considerable implications for our grasp of Athenian imperial behaviour, leading historians to a more profound understanding of one of the most momentous periods of ancient history 28 , 97 . The fact that Ithaca was trained on the largest available dataset of Greek epigraphic texts makes it possible to challenge or overcome individual biases or, indeed, errors in the existing academic tradition, notwithstanding the fact that the dataset in question is originally based on the accumulated academic tradition.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

Ithaca was trained on The Packard Humanities Institute’s Searchable Greek Inscriptions public dataset, PHI, which is available online ( https://inscriptions.packhum.org/ ). The complete processing workflow for transforming the dataset to a machine-actionable format suitable for training Ithaca (I.PHI) is available at GitHub ( https://github.com/sommerschield/iphi ) under Apache License 2.0. The LGPN ( https://www.lgpn.ox.ac.uk/ ) was used by annotators for the onomastics baseline to track the geographical and chronological distribution of ancient names. The PeriodO gazetteer ( https://client.perio.do/ ) was used as a reference for mapping the PHI historical time periods to the chronological range metadata of I.PHI. The Pleiades gazetteer ( https://pleiades.stoa.org/ ) was used as a reference for mapping the PHI region names to the geographical coordinates used in the geographical attribution map visualizations.

Code availability

Ithaca’s training and inference source code is available at GitHub ( https://github.com/deepmind/ithaca ) under Apache License 2.0, along with the trained weights, licensed under Creative Commons Attribution-ShareAlike 4.0 International. A public interface for historians using Ithaca for their research (that is, restoration and attribution of Greek inscriptions, use of all visualization tools discussed in the present paper) is available online ( https://ithaca.deepmind.com ). Neural networks were developed with JAX v.0.2.9 ( https://github.com/google/jax/ ), Flax v.0.3.0 ( https://github.com/google/flax ), and Haiku v.0.0.4 ( https://github.com/deepmind/dm-haiku ). The XLA compiler is bundled with JAX and does not have a separate version number. Dataset processing and analysis used Python v.3.7 ( https://www.python.org/ ), NumPy v.1.19.2 ( https://github.com/numpy/numpy ), SciPy v.1.5.2 ( https://www.scipy.org/ ), pandas v.1.1.3 ( https://github.com/pandas-dev/pandas ), beautifulsoup4 v.4.9.0 ( https://www.crummy.com/software/BeautifulSoup/ ) and Google Colab ( https://research.google.com/colaboratory ), which is an online service and does not have a version number. Visualizations were generated using matplotlib v.3.4.2 ( https://matplotlib.org/ ), seaborn v.0.11.1 ( https://seaborn.pydata.org/ ) and GeoPandas v.0.9.0 ( https://geopandas.org/ ).

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Acknowledgements

We acknowledge Ç. Gulçehre, P. Kohli, M. Zaheer and J. Ainslie for their scientific insights; K. Kavukcuoglu and D. Hassabis for reviewing the manuscript; J. Grayston, B. Maynard, R. Cardenas and the staff at Psycle Interactive for developing the backend of the public interface; E. Karakozoglou, A. Berger, A. Vora and D. Chou for their strategic counselling; V. Margaritis and K.-I. Naltsa for their artistic advice; and M. Tsitonas for his legal counsel. We thank R. Bagnall, L. Calvelli, A. Cooley, R. Parker and C. Roueché for their comments and insights; the contributors of PHI, LGPN, PeriodO and Pleiades for the online resources; the staff at the Acropolis museum for the permission to use the photographic reproduction of the Chalcis decree ( IG I 3 40); and the student annotators and baseline evaluators of the Athens University of Economics and Business (MSc in Digital Methods for the Humanities) and the University of Oxford (Faculty of Classics). T.S. acknowledges that this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101026185. T.S. also thanks Harvard University’s Center for Hellenic Studies for its support.

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These authors contributed equally: Yannis Assael, Thea Sommerschield

Authors and Affiliations

DeepMind, London, UK

Yannis Assael, Brendan Shillingford, Mahyar Bordbar & Nando de Freitas

Department of Humanities, Ca’ Foscari University of Venice, Venice, Italy

Thea Sommerschield

Center for Hellenic Studies, Harvard University, Washington, DC, USA

Department of Informatics, Athens University of Economics and Business, Athens, Greece

John Pavlopoulos, Marita Chatzipanagiotou & Ion Androutsopoulos

Faculty of Classics, University of Oxford, Oxford, UK

Jonathan Prag

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Contributions

Y.A. and T.S. are co-first authors and the order of the names is alphabetical. B.S was a major contributor to the project. M.B. contributed to the execution. J. Pavlopoulos, M.C. and I.A. contributed to the dataset analysis, the evaluations and advised the project. J. Prag and N.d.F. supervised the project.

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Correspondence to Yannis Assael or Thea Sommerschield .

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Extended data figures and tables

Extended data fig. 1 raw and processed phi inscription, text and metadata..

A fragmentary early-fifth century sacrificial calendar from the Acropolis of Athens ( IG I 3 234), face A, lines 10-23. In ( a ) transcription of the inscription text as it currently appears in PHI; ( b ) the same text’s processed rendition in I.PHI; ( c ) the unprocessed metadata of this inscription as it currently appears in the PHI dataset; ( d ) the processed metadata rendition in I.PHI.

Extended Data Fig. 2 Geographical distribution of Greek inscriptions in I.PHI.

Each red circle represents a region across the ancient Mediterranean world (84 in total), the circle size is directly proportional to the number of inscriptions found in that region (total inscriptions in I.PHI n = 78,608).

Extended Data Fig. 3 Comparison between Ithaca and the onomastics baseline’s chronological predictions.

The box plot shows the median and the mean distance between the predicted date and the ground-truth time interval, measured in years using the chronological distance metric (see Methods). In this plot, the bounds of the boxes are defined by the first and the third quartiles, and the whiskers by the minimum and maximum values. Ithaca’s mean distance is 2.2x lower than that of the onomastics baseline. Ithaca’s average prediction loss was 29.3 years from the ground-truth interval, while the median prediction loss was only 3 years. The onomastics baseline consists of n = 142 attributions provided by the human annotators.

Extended Data Fig. 4 Restoration performance comparison.

( a ) The original inscription ( IG II² 116) has 378 missing characters. ( b ) The restorations of the missing characters proposed in the authoritative edition by Rhodes - Osborne 2003 for this text 88 , and which we use as ground truths in our evaluation. ( c ) Pythia’s restoration shows 74 mismatches with the Rhodes-Osborne edition, while ( d ) Ithaca’s shows only 45. Correct restorations are highlighted in green, incorrect ones in red.

Extended Data Fig. 5 Restoration and geographical attribution saliency maps.

( a ) The decree ( IG II² 116) from the Acropolis of Athens recording an alliance between the Athenians and the Thessalian federation (360/1 bc ). At each step of the restoration of the missing word “alliance” (συμμαχία), Ithaca is clearly attending to the contextually important words “Athenians” (ʻAθηναίων) and “Thessalians” (Θετταλων). ( b ) The manumission inscription ( BCH 66/67 (1942/3) 82,9) is correctly attributed to the Delphi region (left), and the generated saliency map (right) highlights words correlated to high accuracy predictions from the word statistics table.

Extended Data Fig. 6 PHI vs. Ithaca’s dating distance in years for disputed Athenian decrees.

The box plot shows the median and the mean of the distribution, the bounds of the boxes are defined by the first and the third quartiles and the whiskers by the minimum and maximum values of n = 21 inscriptions. Ithaca’s chronological predictions (average distance of 5 years from the modern “lower” ground truth) compared to PHI meta-data for time intervals (older estimates, average distance of 27 years from the modern ground truth). Lower distance in years is better. Exploiting the features of our full dataset, Ithaca’s predictions are better and closer to modern re-evaluations compared to the original PHI ground-truth dates. The latter reflect the dates assigned by the published editions which PHI is reporting, and which almost all reflect the old three-bar sigma dating. We refer the reader to Extended Data Table 3 for detailed results.

Extended Data Fig. 7 Chalcis decree ( IG I 3 40).

The inscription records an oath of allegiance sworn by the city of Chalcis to Athens. It has been traditionally dated to 446/5 bc based on the 3-bar sigma criterion 28 , but was more recently redated to 424/3 bc 99 . Photograph by kind concession of the Acropolis Museum. Acrop. 6509 © Acropolis Museum (photo: Socratis Mavrommatis).

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Assael, Y., Sommerschield, T., Shillingford, B. et al. Restoring and attributing ancient texts using deep neural networks. Nature 603 , 280–283 (2022). https://doi.org/10.1038/s41586-022-04448-z

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Irene Peirano Garrison teaches “Doing Things with Latin: Syntax and Stylistics.”

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Professor’s research while developing Latin course turns up surprising insights into political, gender, racial, religious identity

In 1888, the process of awarding the prestigious Bowdoin Prize began routinely: Harvard students anonymously submitted their best essays in English or translated into Greek or Latin in hopes of receiving the honor and impressive sum of as much as $100. Contest judges also assessed essays submitted by female students at the University’s “Annex” (later Radcliffe College), but the winners didn’t receive University recognition or funds — an outside donor supplied an award of $30.

By accident or subversion (the events were disputed in The Crimson ), the men’s and women’s papers were submitted together that year, and a classics essay called “The Roman Senate Under the Empire” by E.B. Pearson received top prize. It was quickly discovered that Pearson was actually Miss E.B. Pearson. The faculty swiftly ruled out her piece for contention, and the runner-up (a man) received $75 in her stead. Pearson received the $30 Annex prize, “thus paying $70 outright for the privilege of being a woman,” according to a Boston Post newspaper article from the time.

The incident was one of many surprising and thought-provoking discoveries in Harvard’s history of classics education for Irene Peirano Garrison while she developed the new Latin prose composition course, “Doing Things with Latin: Syntax and Stylistics.”

“Students at Harvard have learned to write in Latin since the founding of the University in 1636, and in planning the course I began by looking at the history of how, why, and to whom the Latin language, and specifically Latin composition, was taught at Harvard,” said Peirano Garrison, Pope Professor of the Latin Language and director of graduate studies in the classics.

Peirano Garrison wanted to include stories like that of Pearson and the gendered restrictions of the Bowdoin Prize along with more traditional aspects of classics pedagogy in the class, because “I realized that one could tell the history of the University, and even arguably of higher education, through the lens of how Latin was taught at Harvard.”

When she joined the Classics Department last January, Peirano Garrison learned that plans were underway to replace longstanding Latin composition courses “Latin H” and “Latin K,” which had been in place since 1948 and had a reputation for being difficult and technical. She saw an opportunity to build a composition syllabus that was “process-oriented, rather than outcome-oriented.”

“I had thought of Latin prose composition as a very old-fashioned exercise, but what I discovered is a complex and fascinating political history and one in which Latin instruction is deeply entangled with political, gender, racial, and religious identity,” she said. “It was important to me to give students opportunities to learn from writing in Latin as much as to learn Latin itself,” she said.

Like in a traditional composition class, students read and translated passages from authors including Cicero and Caesar. Unlike a traditional course, they kept personal journals of their thoughts on what they were learning. They also analyzed the style, syntax, and grammar of original works of antiquity and their English translations to see how texts upheld certain assumptions about identity and behavior.

“I had thought of Latin prose composition as a very old-fashioned exercise, but what I discovered is a complex and fascinating political history.” Irene Peirano Garrison, Pope Professor of the Latin Language

Peirano Garrison noted that many texts used phrasing and syntax that linked masculinity with strength, femininity with beauty, and enslavement with docility.

“Again and again, we saw how very technical and matter-of fact topics of grammar can be politically charged because of assumptions about the learner or the outcome of education,” she said.

That holistic learning focus took the class out of the classroom throughout the semester. Students visited the Old Burying Ground to read tombstone epitaphs for former Harvard presidents and composed an epitaph for the presidents’ wives, many of whom were not given honorary epigraph in Latin. Another trip took them to Sanders Theatre and Memorial Hall to read inscriptions engraved on the walls honoring Harvard students who died fighting in the Union Army during the Civil War. Those inscriptions served as a foundation for an assignment to write an inscription honoring lives lost to COVID-19. A trip to the Institute of Contemporary Art prompted students to write commentary on fashion designer Virgil Abloh’s style and Cicero and Quintilian’s writing on style in the visual and literary arts.

Students refer to Latin texts at Houghton Library. “I could not have done this class without the archivists and librarians,” said Peirano Garrison, pictured leading class.

Students also visited the Houghton, Gutman, and Schlesinger Libraries, and Harvard Archives to read Latin textbooks, student workbooks, Commencement addresses, and other materials from the last four centuries.

“I could not have done this class without the archivists and librarians,” said Peirano Garrison. “They showed me how this could be done and helped us gain access to amazing materials.”

In those settings, the class studied Latin translations written by W.E.B. Du Bois, analyzed classics research papers by feminist poet and Radcliffe student Adrienne Rich ’51, and read historical student works that debated the value of Latin education over the centuries.

“Doing Things with Latin” gave Dante Minutillo ’24, a classics concentrator, a new appreciation for the complexities of the history of education and Latin’s place in the Harvard experience.

“It’s easy to say that everyone at Harvard was really good at Latin in the past, and part of that was because they had a lot more practice. Or you could say that back then everyone respected Latin, and now everyone is criticizing it,” said Minutillo. “But if you actually look at it, there were debates even years ago as to whether Latin should even be taught. So it’s interesting to see how that’s more continuous than it sometimes seems.”

“It was really important to talk about practices of resistance and contestation that can be gleaned from and through the record of Latin education at Harvard,” said Peirano Garrison. “So many of the historical student works in Latin that we read touched on the very topic of education and its purposes. Those students then used the Latin class to question what they saw around them, as did some educators. I wanted my students to see how that happened so they can be attuned to the politics of their own classroom spaces.”

Peirano Garrison’s course is one of many offered in the Classics Department that engages with both ancient and contemporary questions, said David Elmer, Eliot Professor of Greek Literature and department chair.

“Although classics, as a field, is obviously rooted in the past, it speaks to the present in countless ways,” Elmer said. “Our course offerings are heavily shaped by the latest research of our faculty members, and every year we canvass our students to identify emerging areas of interest.”

Some of those avenues of interest are seen in the department’s spring offerings, including Elmer’s course “Classics, Race, and Power,” which covers race and oppression in the Greco-Roman world, and the Gen Ed class “Tragedy Today,” which is taught by Naomi Weiss, Gardner Cowles Associate Professor of the Humanities, and uses ancient Greek tragedy as a lens to investigate current sociopolitical issues including race, conflict, and immigration.

Peirano Garrison said students should, and can, engage with the classics and understand their role in shaping the political, cultural, and academic spaces in which they spend their time.

“Latin is everywhere on campus: on buildings, student notebooks, commonplace books, letters, Commencement speeches and exercises and more,” she said. “Latin also formed the backbone of Western education for centuries, and it’s important to approach this record of cultural hegemony with a questioning spirit, and within that to engage with Latin pedagogy as a political phenomenon.”

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• January 2024

• Nakhchivan State University

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Indian literature

Our editors will review what you’ve submitted and determine whether to revise the article.

• Internet Archive - A History Of Indian Literature,vol.1
• Government of India - Ministry of Culture - Centre for Cultural Resources and Training - Ancient Indian Literature
• Journal of Emerging Technologies and Innovative Research - A Study on Western Literature and Indian Literature
• IndiaNetzone - History of Indian Literature
• Indian literature - Children's Encyclopedia (Ages 8-11)
• Indian literature - Student Encyclopedia (Ages 11 and up)

Recent News

Indian literature , writings of the Indian subcontinent , produced there in a variety of vernacular languages, including Sanskrit , Prakrit , Pali , Bengali , Bihari , Gujarati , Hindi , Kannada , Kashmiri , Malayalam , Oriya , Punjabi , Rajasthani , Tamil , Telugu , Urdu , Lahnda , Siraiki , and Sindhi , among others, as well as in English . The term Indian literature is used here to refer to literature produced across the Indian subcontinent prior to the creation of the Republic of India in 1947 and within the Republic of India after 1947.

A brief treatment of Indian literature follows. For a fuller treatment, see South Asian arts: Literature . See also Islamic arts: Islamic literatures , India: The arts , Pakistan: The arts , and Bangladesh: The arts .

The earliest Indian literature took the form of the canonical Hindu sacred writings, known as the Veda , which were written in Sanskrit. To the Veda were added prose commentaries such as the Brahmanas and the Upanishads . The production of Sanskrit literature extended from about 1500 bce to about 1000 ce and reached its height of development in the 1st to 7th centuries ce . In addition to sacred and philosophical writings, such genres as erotic and devotional lyrics, court poetry , plays, and narrative folktales emerged.

Because Sanskrit was identified with the Brahminical religion of the Vedas, Buddhism and Jainism adopted other literary languages (Pali and Ardhamagadhi, respectively). From these and other related languages emerged the modern languages of northern India. The literature of those languages depended largely on the ancient Indian background, which includes two Sanskrit epic poems, the Mahabharata and Ramayana , as well as the Bhagavata-purana and the other Puranas . In addition, the Sanskrit philosophies were the source of philosophical writing in the later literatures, and the Sanskrit schools of rhetoric were of great importance for the development of court poetry in many of the modern literatures. The South Indian language of Tamil is an exception to this pattern of Sanskrit influence because it had a classical tradition of its own. Urdu and Sindhi are other exceptions.

Beginning in the 19th century, particularly during the height of British control over the subcontinent, Western literary models had an impact on Indian literature, the most striking result being the introduction of the use of vernacular prose on a major scale . Such forms as the novel and short story began to be adopted by Indian writers, as did realism and an interest in social questions and psychological description. A tradition of literature in English was also established in the subcontinent.

Articles on individual literatures of the Indian subcontinent not specified above include Pali literature , Bengali literature , Gujarati literature , Hindi literature , Kannada literature , Punjabi literature , Tamil literature , Telugu literature , Urdu literature , and Sindhi literature .

Ancient Masterpieces of World Literature

A lens to the past.

Join Harvard faculty in this online course to examine how ancient cultures defined themselves through literature and how their vision contributes to our understanding of civilization today.

What You'll Learn

This short literature course, based on the first half of the Masterpieces of World Literature edX MOOC, examines how civilizations and cultures of the ancient world defined themselves through literature and how that literature has continued to contribute to our understanding of those civilizations and cultures today.

Cities, nations, and empires from antiquity through the middle ages drew on foundational histories and myths for their identities, relating these narratives through generations by means of oral-storytelling and new writing technologies. These epics, story collections, and novels, which take a keen interest in heroic travelers, would eventually travel themselves, finding new global audiences as the first works of world literature.

Tracing developments in language, writing, and literary genre, this course also travels in time, from legendary accounts of ancient kings to histories of medieval courts and early-modern exploration. We will stop to consider how all of these texts affected the history of their own eras, but also how they have continued to find new prominence and significance in ours.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will understand:

• The early history of World Literature
• How literary works are transformed by cultural transmission and modern recovery
• How to critically analyze literary works
• The significance of major technological advances in writing

Your Instructors

David Damrosch

Ernest Bernbaum Professor of Literature at Harvard University Read full bio.

Martin Puchner

Byron and Anita Wien Professor of Drama and of English and Comparative Literature at Harvard University Read full bio. 

Course Outline

• Introduction: What is World Literature? (Goethe)
• The Birth of Literature (The Epic of Gilgamesh)
• Homer and the Archeology of the Classical Past (The Odyssey)
• West-Eastern Conversations (The 1001 Nights)
• The Floating World (The Tale of Genji)
• The First National Epic (The Lusíads)

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $209 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums.  Please note that this track does not offer a certificate for learners who earn a passing grade.

Related Courses

The ancient greek hero.

Join Harvard faculty to discover the heroes of ancient Greece through the Homeric Iliad and Odyssey, the tragedies of Sophocles, the dialogues of Plato, and more.

Shakespeare's Life and Work

Moving between the world in which Shakespeare lived and the present day, this course will introduce different kinds of literary analysis that you can use when reading Shakespeare.

Digital Humanities in Practice: From Research Questions to Results

Combine literary research with data science to find answers in unexpected ways. Learn basic coding tools to draw insights from thousands of documents at once.

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Ancient Greek Literature

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This historical survey of Greek literature from 700 BC to 550 AD concentrates on the principal authors and quotes many passages from their work in translation, to allow the reader to form his own impression of its quality, including Homer, Plato, Aristophanes, and Euripides. Attention is drawn both to the elements in Greek literature and attitudes to life which are unfamiliar to us, and to the elements which appeal most powerfully to succeeding generations. Although it is recognized that this appeal lies above all in the most creative and inventive period (700-300 BC), an account is given of the eight hundred years which followed, which saw the impact of earlier inspirations. Poetry, tragedy, comedy, history, science, philosophy, and oratory are all examined through the available literature. This new edition has been revised to take account of recent scholarship, such as the influence of oriental traditions of Greek literature, and includes several new translations and a thoroughly updated bibliography.

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