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Revisiting translation as a method in language teaching and learning

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Yuan Ping, Revisiting translation as a method in language teaching and learning, ELT Journal , 2024;, ccae022, https://doi.org/10.1093/elt/ccae022

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This article begins by defining translation and discussing its use in language education. It then presents a case study of an EFL listening and speaking course taught at a Chinese university that incorporates communicative translation activities, including vocabulary exercises, group presentations, and role-playing. The article concludes by reflecting on the experience of teaching the course and the lessons learnt. It aims to foster a deeper understanding of the relationship between translation and language teaching/learning and to suggest practical applications of this understanding for teachers in diverse educational settings.

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Join the community, add a new evaluation result row, machine translation.

2183 papers with code • 80 benchmarks • 77 datasets

Machine translation is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: Google seq2seq )

Benchmarks Add a Result

--> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		Transformer Cycle (Rev)
		Transformer+BT (ADMIN init)
		PiNMT
		DeLighT
		HWTSC-Teacher-Sim
		fast-noisy-channel-modeling
		Bi-SimCut
		PS-KD
		MADL
		EnViT5 + MTet
		PS-KD
		FLAN 137B (few-shot, k=11)
		ByteNet
		PiNMT
		Attentional encoder-decoder + BPE
		Transformer trained on highly filtered data
		FLAN 137B (few-shot, k=9)
		StrokeNet
		DynamicConv
		PaLM 2
		PaLM 2
		PaLM 2
		PaLM 2
		SeamlessM4T Large
		SeamlessM4T Large
		CT+B/S construction
		tensorflow/tensor2tensor
		Facebook FAIR (ensemble)
		Evolved Transformer Big
		Transformer base + BPE-Dropout
		Transformer base + BPE-Dropout
		Transformer base + BPE-Dropout
		Transformer base + BPE-Dropout
		Baseline (en->sn)
		Larger
		Adaptively Sparse Transformer (alpha-entmax)
		HeadMask (Random-18)
		Random-18
		C2-50k Segmentation
		Seq-KD + Seq-Inter + Word-KD
		Attentional encoder-decoder + BPE
		Attentional encoder-decoder + BPE
		Attentional encoder-decoder + BPE
		Multi-pass backtranslated adapted transformer
		Transformer trained on highly filtered data
		Multi-pass backtranslated adapted transformer
		Multilingual Transformer
		Transformer trained on highly filtered data
		Multilingual Transformer
		CT+B/S construction
		CT+B/S construction
		CT+B/S construction
		BP-Transformer
		Exploiting Mono at Scale (single)
		DeLighT
		Transformer-base
		Transformer-base
		M_C
		M_C
		M_C
		OmniNetP
		OmniNetP
		OmniNetP
		OmniNetP
		fiore
		PENELOPIE Transformers-based NMT (EN2EL)
		PENELOPIE (Transformers-based Greek-to-English NMT)
		slone/mbart-large-51-mul-myv-v1
		slone/mbart-large-51-myv-mul-v1
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		Vega-MT
		T5

Most implemented papers

Attention is all you need.

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

Neural Machine Translation by Jointly Learning to Align and Translate

Neural machine translation is a recently proposed approach to machine translation.

Sequence to Sequence Learning with Neural Networks

Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

Effective Approaches to Attention-based Neural Machine Translation

Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25. 9 BLEU points, an improvement of 1. 0 BLEU points over the existing best system backed by NMT and an n-gram reranker.

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

Convolutional Sequence to Sequence Learning

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks.

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.

Get science-backed answers as you write with Paperpal's Research feature

How to Make Translating Academic Papers Less Challenging

In the early 1900s, if you asked someone to select the language of science, English would have ranked low on the list. German, French, and Italian would have been considered more favorable options, as researchers and scientists often preferred to communicate their ideas in their native languages. However, around 1918, following the end of World War I, English emerged as the predominant scientific language. This shift coincided with the United States’ ascent as a key player in the global scientific community. 1

The world of science has grown by leaps and bounds and changed a lot since then. It has become more inclusive and is no longer considered as the domain of English-speaking researchers alone. There are now up to 7,000 different languages spoken worldwide, and many researchers choose to write their papers in their local or native language instead of English. However, to make research papers in non-English languages visible and accessible to the broader scientific community, it is essential to translate them into English. 2 It is incumbent upon the author to ensure that the translated paper accurately communicates their research findings. Neglecting this responsibility can result in the familiar “lost in translation” scenario. 

Challenges of Translating Research Papers

Translating research papers presents a unique set of challenges because of the need for a deep understanding of the subject matter, adherence to scientific abstracts, complexity of sentence structure, and more. Beyond the linguistic and conceptual challenges, there are also hurdles in terms of cost, time, and quality. Let’s examine each of these factors. 

1.  Cost:  High-quality academic paper translation comes at a cost, especially when dealing with complex academic papers. Hiring native speakers with scientific and technical expertise adds to the expense. Furthermore, the intricacies of academic language may require additional time and effort, further driving up the cost. 

2.  Time:  Academic paper translation is not a swift process and can be time-consuming, especially if the subject is complex and demands accuracy in the translated text. It requires adherence to proper style and structure and meticulous proofreading. As a result, the translation of a quality academic paper demands time and may not be in sync with the urgency often associated with academic publishing and research dissemination. 

3.  Quality:  Maintaining the integrity and quality of the original manuscript is critical. Rushing through the translation process can compromise clarity, potentially leading to errors in interpretation, grammar, or spelling. Achieving the desired quality level requires maintaining a delicate balance between the nuances of both the source and target languages. 

Deploying AI Translation Tools

While general-purpose online AI translation tools are available to researchers for translating academic texts, their output quality remains a limitation. These tools are trained on generic data and lack the specialized knowledge required to navigate scientific jargon and academic language accurately. Additionally, authors often use online AI translators in conjunction with grammar checkers as part of their text refinement process. This is a common approach, where authors seek to validate the reliability of machine translation results by cross-referencing them with a grammar checker. 5   

In addressing the above challenges, specialized online translation platforms are a promising solution for translating research papers. One such platform, Paperpal, offers a powerful alternative to researchers and academics. Paperpsal integrates a “Translate” feature that seamlessly provides academic translation services within Paperpal. What sets this platform apart is its reliance on robust AI systems, particularly neural machine translation, which excels at handling translations across nearly 30 languages. Researchers who have used the tool have highlighted the ability to accurately translate nuances and effectively convey ideas, even within the intricacies of technical contexts.  

Powering Ahead

The world of science is undergoing accelerated change, with more scientists and researchers from different geographies actively solving problems and making new scientific discoveries. Even as the language of science continues to remain English, greater collaboration among other researchers can facilitate even more accelerated scientific progress. Additionally, it is time that the world discovers and recognizes the new Einsteins, Newtons, and Madame Curies that are working tirelessly in different parts of the world. But in order to do so, their works will need to be translated and made available in the common language of science, English.  

References:  

• Why is English The Main Language Of Science?  – Elsevier 
• China rises to first place in most cited papers  – Science.org  
• The Challenges of Translating Academic Texts: Strategies and Solutions  – Training Express 
• Why It’s Best to Ask a Professional When It Comes To Translation  – Elsevier 
• Academic translation simplified! Paperpal introduces the “Translate” feature aimed at ESL researchers  – Paperpal 

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!

Related Reads:

• Scientific Writing Style Guides Explained
• How to Write a Conclusion for Research Papers (with Examples)
• Publish or Perish – Understanding the Importance of Scholarly Publications in Academia
• Presenting Research Data Effectively Through Tables and Figures

Plagiarism Prevention: Why You Need a Plagiarism Check (Even When You Think You Don’t)!

What are the different types of research papers, you may also like, what is the importance of a concept paper..., how to write the first draft of a..., mla works cited page: format, template & examples, how to write a high-quality conference paper, how paperpal’s research feature helps you develop and..., how to write a successful book chapter for..., academic editing: how to self-edit academic text with..., what are scholarly sources and where can you..., how to write a hypothesis types and examples , measuring academic success: definition & strategies for excellence.

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Computer Science > Computation and Language

Title: lexmatcher: dictionary-centric data collection for llm-based machine translation.

Abstract: The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation. However, the area of data collection for instruction fine-tuning in machine translation remains relatively underexplored. In this paper, we present LexMatcher, a simple yet effective method for data collection that leverages bilingual dictionaries to generate a dataset, the design of which is driven by the coverage of senses found in these dictionaries. The dataset comprises a subset retrieved from an existing corpus and a smaller synthesized subset which supplements the infrequent senses of polysemous words. Utilizing LLaMA2 as our base model, our approach outperforms the established baselines on the WMT2022 test sets and also exhibits significant performance improvements in tasks related to word sense disambiguation and specialized terminology translation. These results underscore the effectiveness of LexMatcher in enhancing LLM-based machine translation.

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Through the big top: An exploratory study of circus-based artistic knowledge translation in rural healthcare services, Québec, Canada

• Théberge, Julie
• Smithman, Mélanie Ann
• Turgeon-Pelchat, Catherine
• Tounkara, Fatoumata Korika
• Richard, Véronique
• Aubertin, Patrice
• Léonard, Patrick
• Alami, Hassane
• Singhroy, Diane
• Fleet, Richard

Background The conventional methods and strategies used for knowledge translation (KT) in academic research often fall short in effectively reaching stakeholders, such as citizens, practitioners, and decision makers, especially concerning complex healthcare issues. In response, a growing number of scholars have been embracing arts-based knowledge translation (ABKT) to target a more diverse audience with varying backgrounds and expectations. Despite the increased interest, utilization, and literature on arts-based knowledge translation over the past three decades, no studies have directly compared traditional knowledge translation with arts-based knowledge translation methods. Thus, our study aimed to evaluate and compare the impact of an arts-based knowledge translation intervention–a circus show–with two traditional knowledge translation interventions (webinar and research report) in terms of awareness, accessibility, engagement, advocacy/policy influence, and enjoyment. Methods To conduct this exploratory convergent mixed method study, we randomly assigned 162 participants to one of the three interventions. All three knowledge translation methods were used to translate the same research project: "Rural Emergency 360: Mobilization of decision-makers, healthcare professionals, patients, and citizens to improve healthcare and services in Quebec's rural emergency departments (UR360)." Results The findings revealed that the circus show outperformed the webinar and research report in terms of accessibility and enjoyment, while being equally effective in raising awareness, increasing engagement, and influencing advocacy/policy. Each intervention strategy demonstrates its unique array of strengths and weaknesses, with the circus show catering to a diverse audience, while the webinar and research report target more informed participants. These outcomes underscore the innovative and inclusive attributes of Arts-Based Knowledge translation, showcasing its capacity to facilitate researchers' engagement with a wider array of stakeholders across diverse contexts. Conclusion As a relevant first step and a complementary asset, arts-based knowledge translation holds immense potential in increasing awareness and mobilization around crucial health issues.

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Translation is the process by which a protein is synthesized by the ribosome, using a messenger RNA (mRNA) template.

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Aminoacyl-tRNA synthetases can promote or suppress cancer progression by regulating codon-dependent translation. A study now shows that valine aminoacyl-tRNA synthetase (VARS) promotes therapeutic resistance of melanoma to MAPK pathway inhibitors by enhancing translation of valine-enriched genes, including the fatty acid oxidation gene HADH .

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The first evidence that the formation of protein complexes is linked to protein synthesis was already provided in the early 1960s.

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