When Does Translation Require Context? A Data-driven, Multilingual Exploration (2023.acl-long)
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| Challenge: | Recent studies in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. |
| Approach: | They develop a multilingual discourse-aware benchmark to evaluate model performance on discourse phenomena in a given dataset. |
| Outcome: | The proposed model improves on previously studied phenomena while uncovering others which were not addressed. |
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Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation (2025.findings-emnlp)
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| Challenge: | Large language models excel at machine translation, but the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. |
| Approach: | They examine how different forms of context influence standard MT metrics and specific discourse phenomena such as formality, pronoun selection, and lexical cohesion. |
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Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
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| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
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Measuring and Increasing Context Usage in Context-Aware Machine Translation (2021.acl-long)
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| Challenge: | Recent work in neural machine translation has demonstrated the necessity and feasibility of using inter-sentential context, but it is often not clear how much they actually utilize it at translation time. |
| Approach: | They propose a conditional cross-mutual information metric to quantify usage of context by model architectures that can use it at translation time. |
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Do Context-Aware Translation Models Pay the Right Attention? (2021.acl-long)
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| Challenge: | Context-aware machine translation models fail to leverage contextual information to resolve ambiguous words and pronouns. |
| Approach: | They propose a new dataset that includes supporting context words for 14K translations that professional translators found useful for pronoun disambiguation. |
| Outcome: | The proposed model can automatically disambiguate pronouns and polysemous words when they are not in the same context. |
Challenges in Context-Aware Neural Machine Translation (2023.emnlp-main)
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| Challenge: | despite well-reasoned intuitions, most context-aware neural machine translation models show only modest improvements over sentence-level systems. |
| Approach: | They propose a more realistic setting for document-level translation called paragraph-to-paragraph (PARA2PARA) they collect a dataset of Chinese-English novels to promote future research . |
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On Context Span Needed for Machine Translation Evaluation (2020.lrec-1)
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| Challenge: | a number of common patterns can be observed for context-aware MT evaluation, authors say . document-level evaluations have largely been performed at the sentence level . the definition of what constitutes a "document level" evaluation is still unclear . |
| Approach: | They propose to use a series of surveys to identify the necessary context span . they find common patterns that can be used to draw general guidelines . |
| Outcome: | The proposed evaluations of machine translation systems show that some issues and spans depend on domain and target language. |
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
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| Challenge: | Several recent papers claim to have achieved human parity at sentence-level machine translation. |
| Approach: | They propose to use a dataset with rich discourse annotations to evaluate MT performance . they find that MT outputs differ fundamentally from human translations in terms of latent discourse structures. |
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A Test Set for Discourse Translation from Japanese to English (2020.lrec-1)
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| Challenge: | Compared with a previous study on test sets for English-to-French discourse translation, we needed different approaches because Japanese has zero pronouns and represents different senses in different characters. |
| Approach: | They used a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. |
| Outcome: | The results show that the translation accuracy of Japanese-to-English discourse translation is improved by using context-aware neural machine translation. |
Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark (2025.emnlp-main)
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Chihiro Taguchi, Seng Mai, Keita Kurabe, Yusuke Sakai, Georgina Agyei, Soudabeh Eslami, David Chiang
| Challenge: | Multilingual machine translation (MT) benchmarks are widely used to evaluate the capabilities of modern MT systems. |
| Approach: | They propose to use a multilingual machine translation benchmark to assess the capabilities of modern machine translation systems. |
| Outcome: | The FLORES+ benchmark claims to maintain a translation quality score of over 90% . however, the data in four languages falls short of the 90% quality standard . |
An Empirical Study of In-context Learning in LLMs for Machine Translation (2024.findings-acl)
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| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
| Approach: | They conduct the first of its kind, exhaustive study of in-context learning for machine translation (MT) they establish that ICL is primarily example-driven and not instruction-driven . |
| Outcome: | The proposed model is based on examples and not instruction-driven learning. |