---
title: "A Context-aware Framework for Translation-mediated"
description: "The authors propose a framework that adds bilingual conversational history to LLM-based translation… 77 references, cited by 3 papers here."
authors: ["José Pombal", "Sweta Agrawal", "Emmanouil Zaranis", "Patrick Fernandes", "André F. T. Martins"]
year: 2026
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2026.tacl-1.26/"
doi: "10.1162/tacl.a.639"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pombal-2026-context-aware-framework-translation-mediated/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/pombal-2026-context-aware-framework-translation-mediated/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/) / A Context-aware Framework for Translation-mediated Conversations

# A Context-aware Framework for Translation-mediated Conversations

**Authors:** [José Pombal](https://md.research.elamur.ai/free/en/authors/jose-pombal/), [Sweta Agrawal](https://md.research.elamur.ai/free/en/authors/sweta-agrawal/), Emmanouil Zaranis, [Patrick Fernandes](https://md.research.elamur.ai/free/en/authors/patrick-fernandes/) and [André F. T. Martins](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/) · **Year:** 2026 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2026](https://md.research.elamur.ai/free/en/venues/tacl/2026/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.tacl-1.26/) · [PDF](https://aclanthology.org/2026.tacl-1.26.pdf) · [DOI 10.1162/tacl.a.639](https://doi.org/10.1162/tacl.a.639) · [OpenAlex](https://openalex.org/W4405095192)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/pombal-2026-context-aware-framework-translation-mediated/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Machine translation can connect people, or people and English-centric assistants, who lack a shared language. However, systems that translate each turn in isolation often miss information from earlier in the conversation. The result is literal, inconsistent or wrong output, for example misresolved pronouns, formality or ambiguous words. The authors propose a framework that adds bilingual conversational history to LLM-based translation at two stages. In training, the model is fine-tuned on instructions that include the previous turns of both parties in their original languages. In inference, it uses quality-aware decoding guided by context-aware metrics. They test the framework on customer-support chats and on user-assistant interactions. The resulting model, TowerChat, outperforms GPT-4o and TowerInstruct on several automatic metrics and language pairs. Interpretability analyses show that it relies on the parts of the context that actually resolve ambiguity. This matters for real-time multilingual support and for serving non-English users through strong English LLMs.

## Key points
- The human-human setting covers English ↔ German, French, Portuguese, Korean and Dutch. The human-assistant setting covers English ↔ German, with the assistant working in English behind a translation layer.
- Bilingual context outperforms in-domain training with standard instructions and English-only context. Quality-aware decoding with context reduces context-related errors in 9 of 12 evaluated settings.
- Analysis tools include P-CXMI, the likelihood difference between context-aware and context-free outputs, and PeCoRE saliency. Disambiguating context segments receive almost three times more saliency than other segments.
- Context helps low-quality segments most, and the best window size differs by language pair. Adding more turns does not hurt quality, but gains level off. Datasets, outputs and code are available at github.com/zeppombal/context-aware-mt.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 539 works · 179,533 citations
- [Instituto de Telecomunicações](https://md.research.elamur.ai/free/en/labs/instituto-de-telecomunicacoes/) — Research institute, Lisbon, Portugal · 35 works · 5,247 citations
- [Instituto Superior Técnico](https://md.research.elamur.ai/free/en/universities/instituto-superior-tecnico/) — University, Lisbon, Portugal · 32 works · 4,064 citations
- [University of Lisbon](https://md.research.elamur.ai/free/en/universities/university-of-lisbon/) — University, Lisbon, Portugal · 26 works · 3,589 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

## Cite

Cite as: José Pombal, Sweta Agrawal, Emmanouil Zaranis, Patrick Fernandes and André F. T. Martins. 2026. A Context-aware Framework for Translation-mediated Conversations. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{pombal2026context,
  title = {{A Context-aware Framework for Translation-mediated Conversations}},
  author = {José Pombal, Sweta Agrawal, Emmanouil Zaranis, Patrick Fernandes and André F. T. Martins},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.639},
  url = {https://doi.org/10.1162/tacl.a.639}
}
```

- [← previous WikiVideo: Article Generation from Multiple Videos](https://md.research.elamur.ai/free/en/papers/martin-2026-wikivideo-article-generation-multiple-videos/)
- [next → A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction](https://md.research.elamur.ai/free/en/papers/martinelli-2026-domain-specific-curated-benchmark-entity/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/)

## References

77 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Sweta Agrawal, Amin Farajian, Patrick Fernandes, Ricardo Rei, and André F. T. Martins. 2024. [Assessing the role of context in chat translation evaluation: Is context helpful and under what conditions?](https://md.research.elamur.ai/free/en/papers/agrawal-2024-assessing-role-context-chat-translation/) Transactions of the Association for Computational Linguistics, 12:1250–1267. https://doi.org/10 .1…
2. Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023. [In-context examples selection for machine translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/). In Findings of the Association for Computational Linguistics: ACL 2023, pages 8857–8873, Toronto, Canada. Association for Computational Linguistics. http…
3. Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, and Andre Martins. 2023. Steering large language models for machine translation with finetuning and in-context learning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pag… [find](https://scholar.google.com/scholar?q=Duarte+Alves%2C+Nuno+Guerreiro%2C+Joa%CC%83o+Alves%2C+Jose%CC%81+Pombal%2C+Ricardo+Rei%2C+Jose%CC%81+de+Souza%2C+Pierre+Colombo%2C+and+Andre+Martins%2E+2023%2E+Steering+large+language+models+for+machine+translation+with+finetuning+an)
4. Duarte M. Alves, José Pombal, Nuno M. Guerreiro, Pedro H. Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, José G. C. de Souza, and André F. T. Martins. 2024. [Tower: An open multilingual large language model for translation-related ta…](https://md.research.elamur.ai/free/en/papers/alves-2024-tower-open-multilingual-large-language/)
5. Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Ben Goodrich, Daniel Duckworth, Semih Yavuz, Amit Dubey, Kyu-Young Kim, and Andy Cedilnik. 2019. [Taskmaster-1: Toward a realistic and diverse dialog dataset](https://md.research.elamur.ai/free/en/papers/byrne-2019-taskmaster-1-realistic-diverse-dialog/). In Proceedings of the 2019 Conference on Empirical Methods in Nat…
6. Sheila Castilho and Rebecca Knowles. 2024. A survey of context in neural machine translation and its evaluation. Natural Language Processing, pages 1–31. https://doi.org/10.1017/nlp.2024.7 — [doi:10.1017/nlp.2024.7](https://doi.org/10.1017/nlp.2024.7) [↗](https://doi.org/10.1017/nlp.2024.7)
7. Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, and Aleksander Madry. 2024. Con-textcite: Attributing model generation to context. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https://doi.org/10.52202/079017-3035 [↗](https://doi.org/10.52202/079017-3035)
8. Pierre Colombo, Nathan Noiry, Ekhine Irurozki, and Stéphan Clémençon. 2022. What are the best systems? New perspectives on nlp benchmarking. In Advances in Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Pierre+Colombo%2C+Nathan+Noiry%2C+Ekhine+Irurozki%2C+and+Ste%CC%81phan+Cle%CC%81menc%CC%A7on%2E+2022%2E+What+are+the+best+systems%3F+New+perspectives+on+nlp+benchmarking%2E+In+Advances+in+Neural+Information+Processing+Systems%2E)
9. Donald A. DePalma, Benjamin B. Sargent, and Renato S. Beninatto. 2006. Can’t read, won’t buy: Why language matters on global websites. Lowell, MA: Common Sense Advisory Inc. [find](https://scholar.google.com/scholar?q=Donald+A%2E+DePalma%2C+Benjamin+B%2E+Sargent%2C+and+Renato+S%2E+Beninatto%2E+2006%2E+Can%E2%80%99t+read%2C+won%E2%80%99t+buy%3A+Why+language+matters+on+global+websites%2E+Lowell%2C+MA%3A+Common+Sense+Advisory+Inc%2E)
10. Krishno Dey, Prerona Tarannum, Md. Arid Hasan, Imran Razzak, and Usman Naseem. 2024. [Better to ask in English: Evaluation of large language models on English, low-resource and cross-lingual settings](https://md.research.elamur.ai/free/en/papers/dey-2024-better-ask-english-evaluation-large/). arXiv preprint, arXiv: 2410.13153.
11. Eleftheria Briakou, Navita Goyal, and Marine Carpuat. 2023. Explaining with contrastive phrasal highlighting: A case study in assisting humans to detect translation differences. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11220–11237, Singapore. [find](https://scholar.google.com/scholar?q=Eleftheria+Briakou%2C+Navita+Goyal%2C+and+Marine+Carpuat%2E+2023%2E+Explaining+with+contrastive+phrasal+highlighting%3A+A+case+study+in+assisting+humans+to+detect+translation+differences%2E+In+Proceedings+of+the+)
12. Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lopez de Lacalle, and Mikel Artetxe. 2023. [Do multilingual language models think better in english?](https://md.research.elamur.ai/free/en/papers/etxaniz-2024-do-multilingual-language-models-think/) arXiv preprint arXiv:2308. 01223. https://doi.org/10.18653/v1/2024.naacl-short.46 [↗](https://doi.org/10.18653/v1/2024.naacl-short.46)
13. Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lacalle, and Mikel Artetxe. 2024. [Do multilingual language models think better in English?](https://md.research.elamur.ai/free/en/papers/etxaniz-2024-do-multilingual-language-models-think/) In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short…
14. Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021. Measuring and increasing context usage in context-aware machine translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on NaturalLa… [find](https://scholar.google.com/scholar?q=Patrick+Fernandes%2C+Kayo+Yin%2C+Graham+Neubig%2C+and+Andre%CC%81+F%2E+T%2E+Martins%2E+2021%2E+Measuring+and+increasing+context+usage+in+context%2Daware+machine+translation%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+th)
15. M. Amin Farajian, António V. Lopes, André F. T. Martins, Sameen Maruf, and Gholamreza Haffari. 2020. [Findings of the WMT 2020 shared task on chat translation](https://md.research.elamur.ai/free/en/papers/farajian-2020-findings-wmt-2020-shared-task/). In Proceedings of the Fifth Conference on Machine Translation, pages 65–75, Online. Association for Computational Linguistics. https://doi…
16. Mara Finkelstein and Markus Freitag. 2024. [MBR and QE finetuning: Training-time distillation of the best and most expensive decoding methods](https://md.research.elamur.ai/free/en/papers/finkelstein-2023-mbr-qe-finetuning-training-time/). In The Twelfth International Conference on Learning Representations.
17. António Farinhas, José de Souza, and Andre Martins. 2023. An empirical study of translation hypothesis ensembling with large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11956–11970, Singapore. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Anto%CC%81nio+Farinhas%2C+Jose%CC%81+de+Souza%2C+and+Andre+Martins%2E+2023%2E+An+empirical+study+of+translation+hypothesis+ensembling+with+large+language+models%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Metho)
18. Markus Freitag, Behrooz Ghorbani, and Patrick Fernandes. 2023a. Epsilon sampling rocks: Investigating sampling strategies for minimum Bayes risk decoding for machine translation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9198–9209, Singapore. Association for Com… [find](https://scholar.google.com/scholar?q=Markus+Freitag%2C+Behrooz+Ghorbani%2C+and+Patrick+Fernandes%2E+2023a%2E+Epsilon+sampling+rocks%3A+Investigating+sampling+strategies+for+minimum+Bayes+risk+decoding+for+machine+translation%2E+In+Findings+of+the+As)
19. António Farinhas, Nuno M Guerreiro, Sweta Agrawal, Ricardo Rei, and André F. T. Martins. 2025. Translate smart, not hard: Cascaded translation systems with quality-aware deferral. arXiv preprint arXiv:2502.12701. https://doi.org/10.18653/v1/2025 .emnlp-main.1358 [↗](https://doi.org/10.18653/v1/2025)
20. Markus Freitag, David Grangier, Qijun Tan, and Bowen Liang. 2022. [High quality rather than high model probability: Minimum Bayes risk decoding with neural metrics](https://md.research.elamur.ai/free/en/papers/freitag-2022-high-quality-rather-than-high/). Transactions of the Association for Computational Linguistics, 10:811–825. https://doi.org/10 .1162/tacl_a_00491
21. Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022. [Quality-aware decoding for neural machine translation](https://md.research.elamur.ai/free/en/papers/fernandes-2022-quality-aware-decoding-neural-machine/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: H…
22. Markus Freitag, Nitika Mathur, Chi-kiu Lo, Eleftherios Avramidis, Ricardo Rei, Brian Thompson, Tom Kocmi, Frederic Blain, Daniel Deutsch, Craig Stewart, Chrysoula Zerva, Sheila Castilho, Alon Lavie, and George Foster. 2023b. [Results of wmt23 metrics shared task: Metrics might be guilty but reference…](https://md.research.elamur.ai/free/en/papers/freitag-2023-results-wmt23-metrics-shared-task/)
23. Patrick Fernandes, Kayo Yin, Emmy Liu, André Martins, and Graham Neubig. 2023. [When does translation require context?](https://md.research.elamur.ai/free/en/papers/ghazvininejad-2023-dictionary-based-phrase-level-prompting/) A data-driven, multilingual exploration. In Pro-Marjan Ghazvininejad, Hila Gonen, and Luke Zettlemoyer. 2023. Dictionary-based phrase-level prompting of large language models for ma…
24. States. Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.naacl[-main.111](https://md.research.elamur.ai/free/en/papers/jiang-2022-blonde-automatic-evaluation-metric-document/) [↗](https://doi.org/10.18653/v1/2022.naacl-main.111)
25. Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, and Srijan Kumar. 2024. Better to ask in English: Cross-lingual evaluation of large language models for healthcare queries. In Proceedings of the ACM on Web Conference 2024, pages 2627–2638. https://doi.org/10 .1145/3589334.36456… [find](https://scholar.google.com/scholar?q=Yiqiao+Jin%2C+Mohit+Chandra%2C+Gaurav+Verma%2C+Yibo+Hu%2C+Munmun+De+Choudhury%2C+and+Srijan+Kumar%2E+2024%2E+Better+to+ask+in+English%3A+Cross%2Dlingual+evaluation+of+large+language+models+for+healthcare+queries%2E+In+Pr)
26. Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Yujiu Yang, Rui Wang, Zhaopeng Tu, Shuming Shi, and Xing Wang. 2024. [Exploring human-like translation strategy with large language models](https://md.research.elamur.ai/free/en/papers/he-2024-exploring-human-like-translation-strategy/). Transactions of the Association for Computational Linguistics, 12:229–246. https://doi.org/10 .1162/tacl_a…
27. Juraj Juraska, Mara Finkelstein, Daniel Deutsch, Aditya Siddhant, Mehdi Mirzazadeh, and Markus Freitag. 2023. [MetricX-23: The Google submission to the WMT 2023 metrics shared task](https://md.research.elamur.ai/free/en/papers/juraska-2023-metricx-23-google-submission-wmt/). In Proceedings of the Eighth Conference on Machine Translation, pages 756–767, Singapore. Association for Computational…
28. John Hewitt, Christopher Manning, and Percy Liang. 2022. [Truncation sampling as language model desmoothing](https://md.research.elamur.ai/free/en/papers/hewitt-2022-truncation-sampling-as-language-model/). In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3414–3427, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. https://doi.org/10 .18653…
29. Marzena Karpinska and Mohit Iyyer. 2023. [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://md.research.elamur.ai/free/en/papers/karpinska-2023-large-language-models-effectively-leverage/). In Proceedings of the Eighth Conference on Machine Translation, pages 419–451, Singapore. Association for Computational Linguistics. https…
30. Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Anna Korhonen, and Ivan Vulić. 2023a. A systematic study of performance disparities in multilingual task-oriented dialogue systems. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing… [find](https://scholar.google.com/scholar?q=Songbo+Hu%2C+Han+Zhou%2C+Moy+Yuan%2C+Milan+Gritta%2C+Guchun+Zhang%2C+Ignacio+Iacobacci%2C+Anna+Korhonen%2C+and+Ivan+Vulic%CC%81%2E+2023a%2E+A+systematic+study+of+performance+disparities+in+multilingual+task%2Doriented+dialogu)
31. Diederik P. Kingma and Jimmy Ba. 2015. [Adam: A method for stochastic optimization](https://md.research.elamur.ai/free/en/papers/kingma-2014-adam-method-stochastic-optimization/). In International Conference on Learning Representations (ICLR).
32. Xinyu Hu, Xunjian Yin, and Xiaojun Wan. 2023b. Exploring context-aware evaluation metrics for machine translation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15291–15298, Singapore. Association for Computational Linguistics. https://doi.org/10.18653/v1 /2023.find… [find](https://scholar.google.com/scholar?q=Xinyu+Hu%2C+Xunjian+Yin%2C+and+Xiaojun+Wan%2E+2023b%2E+Exploring+context%2Daware+evaluation+metrics+for+machine+translation%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pages+15291)
33. Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Philipp Koehn, Benjamin Marie, Christof Monz, Makoto Morishita, Kenton Murray, Makoto Nagata, Toshiaki Nakazawa, Martin… [find](https://scholar.google.com/scholar?q=Tom+Kocmi%2C+Eleftherios+Avramidis%2C+Rachel+Bawden%2C+Ondr%CC%8Cej+Bojar%2C+Anton+Dvorkovich%2C+Christian+Federmann%2C+Mark+Fishel%2C+Markus+Freitag%2C+Thamme+Gowda%2C+Roman+Grundkiewicz%2C+Barry+Haddow%2C+Philipp+Koehn%2C+Benja)
34. Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, and Ming Zhou. 2022. [BlonDe: An automatic evaluation metric for document-level machine translation](https://md.research.elamur.ai/free/en/papers/jiang-2022-blonde-automatic-evaluation-metric-document/). In Proceedings of the 2022 Conference of the North American Chapter of t…
35. Nikita Mehandru, Sweta Agrawal, Yimin Xiao, Ge Gao, Elaine Khoong, Marine Carpuat, and Niloufar Salehi. 2023. Physician detection of clinical harm in machine translation: Quality estimation aids in reliance and backtranslation identifies critical errors. In Proceedings of the 2023 Conference on Empi… [find](https://scholar.google.com/scholar?q=Nikita+Mehandru%2C+Sweta+Agrawal%2C+Yimin+Xiao%2C+Ge+Gao%2C+Elaine+Khoong%2C+Marine+Carpuat%2C+and+Niloufar+Salehi%2E+2023%2E+Physician+detection+of+clinical+harm+in+machine+translation%3A+Quality+estimation+aids+in+re)
36. Lea Krause, Selene Baez Santamaria, and Jan-Christoph Kalo. 2024. Graph representations for machine translation in dialogue settings. In Proceedings of the Ninth Conference on Machine Translation, pages 1038–1046, Miami, Florida, USA. Association for Computational Linguistics. https://doi.org/10 .18… [find](https://scholar.google.com/scholar?q=Lea+Krause%2C+Selene+Baez+Santamaria%2C+and+Jan%2DChristoph+Kalo%2E+2024%2E+Graph+representations+for+machine+translation+in+dialogue+settings%2E+In+Proceedings+of+the+Ninth+Conference+on+Machine+Translation%2C+pag)
37. John Mendonca, Patrı́cia Pereira, Miguel Menezes, Vera Cabarrão, Ana C. Farinha, Helena Moniz, Alon Lavie, and Isabel Trancoso. 2023. Dialogue quality and emotion annotations for customer support conversations. In Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Met… [find](https://scholar.google.com/scholar?q=John+Mendonca%2C+Patr%C4%B1%CC%81cia+Pereira%2C+Miguel+Menezes%2C+Vera+Cabarra%CC%83o%2C+Ana+C%2E+Farinha%2C+Helena+Moniz%2C+Alon+Lavie%2C+and+Isabel+Trancoso%2E+2023%2E+Dialogue+quality+and+emotion+annotations+for+customer+support+con)
38. Yunlong Liang, Fandong Meng, Yufeng Chen, Jinan Xu, and Jie Zhou. 2021. Modeling bilingual conversational characteristics for neural chat translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Lan… [find](https://scholar.google.com/scholar?q=Yunlong+Liang%2C+Fandong+Meng%2C+Yufeng+Chen%2C+Jinan+Xu%2C+and+Jie+Zhou%2E+2021%2E+Modeling+bilingual+conversational+characteristics+for+neural+chat+translation%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+)
39. Wafaa Mohammed, Sweta Agrawal, Amin Farajian, Vera Cabarrão, Bryan Eikema, Ana C. Farinha, and José G. C. De Souza. 2024. Findings of the WMT 2024 shared task on chat translation. In Proceedings of the Ninth Conference on Machine Translation, pages 701–714, Miami, Florida, USA. Association for Com… [find](https://scholar.google.com/scholar?q=Wafaa+Mohammed%2C+Sweta+Agrawal%2C+Amin+Farajian%2C+Vera+Cabarra%CC%83o%2C+Bryan+Eikema%2C+Ana+C%2E+Farinha%2C+and+Jose%CC%81+G%2E+C%2E+De+Souza%2E+2024%2E+Findings+of+the+WMT+2024+shared+task+on+chat+translation%2E+In+Proceedings+of+)
40. Chaoqun Liu, Wenxuan Zhang, Yiran Zhao, Anh Tuan Luu, and Lidong Bing. 2024. [Is translation all you need? A study on solving multilingual tasks with large language models](https://md.research.elamur.ai/free/en/papers/liu-2025-is-translation-all-you-need/). arXiv preprint arXiv:2403.10258.
41. António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020. [Document-level neural MT: A systematic comparison](https://md.research.elamur.ai/free/en/papers/lopes-2020-document-level-neural-mt-systematic/). In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 225–234, Lisboa, Portugal. European Association f…
42. Yasmin Moslem, Gianfranco Romani, Mahdi Molaei, John D. Kelleher, Rejwanul Haque, and Andy Way. 2023. Domain terminology integration into machine translation: Leveraging large language models. In Proceedings of the Eighth Conference on Machine Translation, pages 902–911, Singapore. Association for C… [find](https://scholar.google.com/scholar?q=Yasmin+Moslem%2C+Gianfranco+Romani%2C+Mahdi+Molaei%2C+John+D%2E+Kelleher%2C+Rejwanul+Haque%2C+and+Andy+Way%2E+2023%2E+Domain+terminology+integration+into+machine+translation%3A+Leveraging+large+language+models%2E+In+Proc)
43. Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2018. Contextual neural model for translating bilingual multi-speaker conversations. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 101–112, Brussels, Belgium. Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Sameen+Maruf%2C+Andre%CC%81+F%2E+T%2E+Martins%2C+and+Gholamreza+Haffari%2E+2018%2E+Contextual+neural+model+for+translating+bilingual+multi%2Dspeaker+conversations%2E+In+Proceedings+of+the+Third+Conference+on+Machine+Trans)
44. Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, and Jingbo Zhu. 2023. Augmenting large language model translators via translation memories. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10287–10299, Toronto, Can… [find](https://scholar.google.com/scholar?q=Yongyu+Mu%2C+Abudurexiti+Reheman%2C+Zhiquan+Cao%2C+Yuchun+Fan%2C+Bei+Li%2C+Yinqiao+Li%2C+Tong+Xiao%2C+Chunliang+Zhang%2C+and+Jingbo+Zhu%2E+2023%2E+Augmenting+large+language+model+translators+via+translation+memories%2E+In+)
45. Sameen Maruf, Fahimeh Saleh, and Gholamreza Haffari. 2021. A survey on document-level neural machine translation: Methods and evaluation. ACM Computing Surveys, 54(2). https://doi.org/10.1145/3441691 [↗](https://doi.org/10.1145/3441691)
46. Artur Nowakowski, Gabriela Pałka, Kamil Guttmann, and Mikołaj Pokrywka. 2022. Adam Mickiewicz University at WMT 2022: NER-assisted and quality-aware neural machine translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 326–334, Abu Dhabi, United Arab Emirates (Hybr… [find](https://scholar.google.com/scholar?q=Artur+Nowakowski%2C+Gabriela+Pa%C5%82ka%2C+Kamil+Guttmann%2C+and+Miko%C5%82aj+Pokrywka%2E+2022%2E+Adam+Mickiewicz+University+at+WMT+2022%3A+NER%2Dassisted+and+quality%2Daware+neural+machine+translation%2E+In+Proceedings+of+the+S)
47. Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim, and Arianna Bisazza. 2024. Quantifying the plausibility of context reliance in neural machine translation. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Gabriele+Sarti%2C+Grzegorz+Chrupa%C5%82a%2C+Malvina+Nissim%2C+and+Arianna+Bisazza%2E+2024%2E+Quantifying+the+plausibility+of+context+reliance+in+neural+machine+translation%2E+In+The+Twelfth+International+Conference+on)
48. Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, and Orhan Firat. 2023. Interactive-chain-prompting: Ambiguity resolution for crosslingual conditional generation with interaction. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of t… [find](https://scholar.google.com/scholar?q=Jonathan+Pilault%2C+Xavier+Garcia%2C+Arthur+Braz%CC%8Cinskas%2C+and+Orhan+Firat%2E+2023%2E+Interactive%2Dchain%2Dprompting%3A+Ambiguity+resolution+for+crosslingual+conditional+generation+with+interaction%2E+In+Proceedings+o)
49. Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Wen-tau Yih. 2024. [Trusting your evidence: Hallucinate less with context-aware decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/). In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language…
50. Maja Popović. 2015. [chrF: character n-gram F-score for automatic MT evaluation](https://md.research.elamur.ai/free/en/papers/popovic-2015-chrf-character-n-gram-f/). In Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 392–395, Lisbon, Portugal. Association for Computational Linguistics. https://doi.org/10 .18653/v1/W15-3049
51. Daria Sinitsyna and Konstantin Savenkov. 2024. Comparative evaluation of large language models for linguistic quality assessment in machine translation. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 154–183, Chicago,… [find](https://scholar.google.com/scholar?q=Daria+Sinitsyna+and+Konstantin+Savenkov%2E+2024%2E+Comparative+evaluation+of+large+language+models+for+linguistic+quality+assessment+in+machine+translation%2E+In+Proceedings+of+the+16th+Conference+of+the+As)
52. Ricardo Rei, José G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André F. T. Martins. 2022. [COMET-22: Unbabel-IST 2022 submission for the metrics shared task](https://md.research.elamur.ai/free/en/papers/rei-2022-comet-22-unbabel-ist-2022/). In Proceedings of the Seventh Conference on Machine Translation (WMT), page…
53. Felix Stahlberg, Adrià de Gispert, Eva Hasler, and Bill Byrne. 2017. Neural machine translation by minimising the Bayes-risk with respect to syntactic translation lattices. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Shor… [find](https://scholar.google.com/scholar?q=Felix+Stahlberg%2C+Adria%CC%80+de+Gispert%2C+Eva+Hasler%2C+and+Bill+Byrne%2E+2017%2E+Neural+machine+translation+by+minimising+the+Bayes%2Drisk+with+respect+to+syntactic+translation+lattices%2E+In+Proceedings+of+the+15th)
54. Raphael Reinauer, Patrick Simianer, Kaden Uhlig, Johannes E. M. Mosig, and Joern Wuebker. 2023. Neural machine translation models can learn to be few-shot learners. arXiv preprint, arXiv:2309.08590. — [arXiv:2309.08590](https://arxiv.org/abs/2309.08590) [find](https://scholar.google.com/scholar?q=Raphael+Reinauer%2C+Patrick+Simianer%2C+Kaden+Uhlig%2C+Johannes+E%2E+M%2E+Mosig%2C+and+Joern+Wuebker%2E+2023%2E+Neural+machine+translation+models+can+learn+to+be+few%2Dshot+learners%2E+arXiv+preprint%2C+arXiv%3A2309%2E08590%2E+%E2%80%94)
55. Mingi Sung, Seungmin Lee, Jiwon Kim, and Sejoon Kim. 2024. Context-aware LLM translation system using conversation summarization and dialogue history. In Proceedings of the Ninth Conference on Machine Translation, pages 1011–1015, Miami, Florida, USA. Association for Computational Linguistics. https… [find](https://scholar.google.com/scholar?q=Mingi+Sung%2C+Seungmin+Lee%2C+Jiwon+Kim%2C+and+Sejoon+Kim%2E+2024%2E+Context%2Daware+LLM+translation+system+using+conversation+summarization+and+dialogue+history%2E+In+Proceedings+of+the+Ninth+Conference+on+Machine)
56. Samantha Robertson and Mark Dı́az. 2022. Understanding and being understood: User strategies for identifying and recovering from mistranslations in machine translation-mediated Firas Trabelsi, David Vilar, Mara Finkelstein, and Markus Freitag. 2024. Efficient minimum bayes risk decoding using low-ra… [find](https://scholar.google.com/scholar?q=Samantha+Robertson+and+Mark+D%C4%B1%CC%81az%2E+2022%2E+Understanding+and+being+understood%3A+User+strategies+for+identifying+and+recovering+from+mistranslations+in+machine+translation%2Dmediated+Firas+Trabelsi%2C+David+V)
57. Elena Voita, Rico Sennrich, and Ivan Titov. 2019. [When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion](https://md.research.elamur.ai/free/en/papers/voita-2019-when-good-translation-is-wrong/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1198–1212, Florence,…
58. Marcos Treviso, Nuno M. Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan van Stigt, and André F. T. Martins. 2024. xTower: A multilingual LLM for explaining and correcting translation errors. arXiv preprint, arXiv: 2406.19482. https://doi.org/10.18653 /… [find](https://scholar.google.com/scholar?q=Marcos+Treviso%2C+Nuno+M%2E+Guerreiro%2C+Sweta+Agrawal%2C+Ricardo+Rei%2C+Jose%CC%81+Pombal%2C+Tania+Vaz%2C+Helena+Wu%2C+Beatriz+Silva%2C+Daan+van+Stigt%2C+and+Andre%CC%81+F%2E+T%2E+Martins%2E+2024%2E+xTower%3A+A+multilingual+LLM+for+explain)
59. Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023. [Document-level machine translation with large language models](https://md.research.elamur.ai/free/en/papers/wang-2023-document-level-machine-translation-large/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16646–16661, Singapore. Association for Co…
60. Jun-ichi Tsujii and Makoto Nagao. 1988. Dialogue translation vs. text translation. In Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics. https://doi.org/10.3115/991719.991778 [↗](https://doi.org/10.3115/991719.991778)
61. Longyue Wang, Zhaopeng Tu, Andy Way, and Qun Liu. 2017. [Exploiting cross-sentence context for neural machine translation](https://md.research.elamur.ai/free/en/papers/wang-2017-exploiting-cross-sentence-context-neural/). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2826–2831, Copenhagen, Denmark. Association for Computational Linguistics. https:…
62. Jannis Vamvas and Rico Sennrich. 2024. Linear-time minimum Bayes risk decoding with reference aggregation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 790–801, Bangkok, Thailand. Association for Computational Linguistics.… [find](https://scholar.google.com/scholar?q=Jannis+Vamvas+and+Rico+Sennrich%2E+2024%2E+Linear%2Dtime+minimum+Bayes+risk+decoding+with+reference+aggregation%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28V)
63. Longyue Wang, Xiaojun Zhang, Zhaopeng Tu, Andy Way, and Qun Liu. 2016. Automatic construction of discourse corpora for dialogue translation. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2748–2754, Portorož, Slovenia. European Language Re… [find](https://scholar.google.com/scholar?q=Longyue+Wang%2C+Xiaojun+Zhang%2C+Zhaopeng+Tu%2C+Andy+Way%2C+and+Qun+Liu%2E+2016%2E+Automatic+construction+of+discourse+corpora+for+dialogue+translation%2E+In+Proceedings+of+the+Tenth+International+Conference+on+Lan)
64. Giorgos Vernikos, Brian Thompson, Prashant Mathur, and Marcello Federico. 2022. [Embarrassingly easy document-level MT metrics: How to convert any pretrained metric into a document-level metric](https://md.research.elamur.ai/free/en/papers/vernikos-2022-embarrassingly-easy-document-level-mt/). In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 118–128, Abu Dhabi, United Ar…
65. Bonnie Webber, Andrei Popescu-Belis, Katja Markert, and Jörg Tiedemann, editors. 2013. Proceedings of the Workshop on Discourse in Machine Translation. Association for Computational Linguistics, Sofia, Bulgaria. [find](https://scholar.google.com/scholar?q=Bonnie+Webber%2C+Andrei+Popescu%2DBelis%2C+Katja+Markert%2C+and+Jo%CC%88rg+Tiedemann%2C+editors%2E+2013%2E+Proceedings+of+the+Workshop+on+Discourse+in+Machine+Translation%2E+Association+for+Computational+Linguistics%2C+Sofi)
66. Xiangpeng Wei, Haoran Wei, Huan Lin, Tianhao Li, Pei Zhang, Xingzhang Ren, Mei Li, Yu Wan, Zhiwei Cao, Binbin Xie, Tianxiang Hu, Shangjie Li, Binyuan Hui, Bowen Yu, Dayiheng Liu, Baosong Yang, Fei Huang, and Jun Xie. 2023. Polylm: An open source polyglot large language model. [find](https://scholar.google.com/scholar?q=Xiangpeng+Wei%2C+Haoran+Wei%2C+Huan+Lin%2C+Tianhao+Li%2C+Pei+Zhang%2C+Xingzhang+Ren%2C+Mei+Li%2C+Yu+Wan%2C+Zhiwei+Cao%2C+Binbin+Xie%2C+Tianxiang+Hu%2C+Shangjie+Li%2C+Binyuan+Hui%2C+Bowen+Yu%2C+Dayiheng+Liu%2C+Baosong+Yang%2C+Fei+Hua)
67. Sebastian T. Vincent, Loı̈c Barrault, and Carolina Scarton. 2022. Controlling extra-textual attributes about dialogue participants: A case study of English-to-Polish neural machine translation. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 12… [find](https://scholar.google.com/scholar?q=Sebastian+T%2E+Vincent%2C+Lo%C4%B1%CC%88c+Barrault%2C+and+Carolina+Scarton%2E+2022%2E+Controlling+extra%2Dtextual+attributes+about+dialogue+participants%3A+A+case+study+of+English%2Dto%2DPolish+neural+machine+translation%2E+In+Pro)
68. Minghao Wu, Thuy-Trang Vu, Lizhen Qu, George Foster, and Gholamreza Haffari. 2024. [find](https://scholar.google.com/scholar?q=Minghao+Wu%2C+Thuy%2DTrang+Vu%2C+Lizhen+Qu%2C+George+Foster%2C+and+Gholamreza+Haffari%2E+2024%2E)
69. Adapting large language models for document-level machine translation. arXiv preprint arXiv:2401.06468. — [arXiv:2401.06468](https://arxiv.org/abs/2401.06468) [find](https://scholar.google.com/scholar?q=Adapting+large+language+models+for+document%2Dlevel+machine+translation%2E+arXiv+preprint+arXiv%3A2401%2E06468%2E+%E2%80%94+%5BarXiv%3A2401%2E06468%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2401%2E06468%29)
70. Yongle Zhang, Dennis Asamoah Owusu, Marine Carpuat, and Ge Gao. 2022. Facilitating global team meetings between language-based subgroups: When and how can machine translation help? Proceedings of ACM on Human-Computer Interaction, 6(CSCW1). https://doi.org/10.1145/3512937 — [doi:10.1145/3512937](https://doi.org/10.1145/3512937) [↗](https://doi.org/10.1145/3512937)
71. Kayo Yin and Graham Neubig. 2022. Interpreting language models with contrastive explanations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 184–198, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. https://doi.org/10 .1865… [find](https://scholar.google.com/scholar?q=Kayo+Yin+and+Graham+Neubig%2E+2022%2E+Interpreting+language+models+with+contrastive+explanations%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+184%E2%80%93198%2C+)
72. Yongle Zhang, Dennis Asamoah Owusu, Emily Gong, Shaan Chopra, Marine Carpuat, and Ge Gao. 2021. Leveraging machine translation to support distributed teamwork between language-based subgroups: The effects of automated keyword tagging. In Extended Abstracts of the 2021 CHI Conference on Human Factors… — [doi:10.1145/3411763](https://doi.org/10.1145/3411763) [find](https://scholar.google.com/scholar?q=Yongle+Zhang%2C+Dennis+Asamoah+Owusu%2C+Emily+Gong%2C+Shaan+Chopra%2C+Marine+Carpuat%2C+and+Ge+Gao%2E+2021%2E+Leveraging+machine+translation+to+support+distributed+teamwork+between+language%2Dbased+subgroups%3A+The+eff)
73. Biao Zhang, Barry Haddow, and Alexandra Birch. 2023a. [Prompting large language model for machine translation: A case study](https://md.research.elamur.ai/free/en/papers/zhang-2023-prompting-large-language-model-machine/). In International Conference on Machine Learning, pages 41092–41110. PMLR.
74. Bryan Zhang and Amita Misra. 2022. Machine translation impact in E-commerce multilingual search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 99–109, Abu Dhabi, UAE. Association for Computational Linguistics. https://doi.org/10 .186… [find](https://scholar.google.com/scholar?q=Bryan+Zhang+and+Amita+Misra%2E+2022%2E+Machine+translation+impact+in+E%2Dcommerce+multilingual+search%2E+In+Proceedings+of+the+2022+Conference+on+Empirical+Methods+in+Natural+Language+Processing%3A+Industry+Tra)
75. Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, and Lidong Bing. 2024. [How do large language models handle multilingualism?](https://md.research.elamur.ai/free/en/papers/zhao-2024-large-language-models-handle-multilingualism/) arXiv preprint arXiv:2402.18815.
76. Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2024. [Multilingual machine translation with large language models: Empirical results and analysis](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/). In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2765–2781, Mexi…
77. Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang, Zhengrui Ma, Yan Zhou, Langlin Huang, Mengyu Bu, Shangtong Gui, Yunji Chen, Xilin Chen, and Yang Feng. 2023b. Bayling: Bridging cross-lingual alignment and instruction following through interactive translation for large language models. arXiv preprint, a… [find](https://scholar.google.com/scholar?q=Shaolei+Zhang%2C+Qingkai+Fang%2C+Zhuocheng+Zhang%2C+Zhengrui+Ma%2C+Yan+Zhou%2C+Langlin+Huang%2C+Mengyu+Bu%2C+Shangtong+Gui%2C+Yunji+Chen%2C+Xilin+Chen%2C+and+Yang+Feng%2E+2023b%2E+Bayling%3A+Bridging+cross%2Dlingual+alignment+an)

*Updated 31 October 2026*