🔗 Who cites «Does Neural Machine Translation Benefit from Larger Context?»

87 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← back to «Does Neural Machine Translation Benefit from Larger Context?»

1. Context-Aware Neural Machine Translation Learns Anaphora ResolutionAnnual Meeting of the Association for Computational Linguistics (ACL)Elena Voita, Pavel Serdyukov, Rico Sennrich, et al.2018 · 449 citations
2. Document-Level Neural Machine Translation with Hierarchical Attention NetworksConference on Empirical Methods in Natural Language Processing (EMNLP)Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, et al.2018 · 415 citations
3. When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical CohesionAnnual Meeting of the Association for Computational Linguistics (ACL)Elena Voita, Rico Sennrich, Ivan Titov2019 · 323 citations
4. Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defenseConference on Neural Information Processing Systems (NeurIPS)Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.2023 · 310 citations
5. Exploiting Cross-Sentence Context for Neural Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Longyue Wang, Zhaopeng Tu, Andy Way, et al.2017 · 293 citations
6. Selective Attention for Context-aware Neural Machine TranslationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Sameen Maruf, André F. T. Martins, Gholamreza Haffari2019 · 263 citations
7. Evaluating Discourse Phenomena in Neural Machine TranslationarXiv:1711.00513Rachel Bawden, Rico Sennrich, Alexandra Birch, et al.2017 · 253 citations
8. Document Context Neural Machine Translation with Memory NetworksAnnual Meeting of the Association for Computational Linguistics (ACL)Sameen Maruf, Gholamreza Haffari2018 · 238 citations
9. Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors PersistConference on Machine Translation (WMT)Marzena Karpinska, Mohit Iyyer2023 · 156 citations
10. A Simple and Effective Unified Encoder for Document-Level Machine TranslationAnnual Meeting of the Association for Computational Linguistics (ACL)Shuming Ma, Dongdong Zhang, Ming Zhou2020 · 136 citations
11. When and Why is Document-level Context Useful in Neural Machine Translation?Workshop on Discourse in Machine TranslationYunsu Kim, Duc Thanh Tran, Hermann Ney2019 · 124 citations
12. Better Document-Level Machine Translation with Bayes' RuleTransactions of the Association for Computational Linguistics (TACL)Lei Yu, Laurent Sartran, Wojciech Stokowiec, et al.2020 · 72 citations
13. Coreference and Coherence in Neural Machine Translation: A Study Using Oracle ExperimentsConference on Machine Translation: Research PapersDario Stojanovski, Alexander Fraser2018 · 46 citations
14. One Model to Learn Both: Zero Pronoun Prediction and TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Longyue Wang, Zhaopeng Tu, Xing Wang, et al.2019 · 41 citations
15. Findings of the WMT 2020 Shared Task on Chat TranslationConference on Machine Translation (WMT)M. Amin Farajian, António V. Lopes, André F. T. Martins, et al.2020 · 29 citations
16. Assessing the Role of Context in Chat Translation Evaluation: Is Context Helpful and Under What Conditions?Transactions of the Association for Computational Linguistics (TACL)Sweta Agrawal, Amin Farajian, Patrick Fernandes, et al.2024 · 8 citations
17. Context-8: A Data Set for Evaluating Context Sensitivity in Machine TranslationLanguage Resources and Evaluation Conference (LREC)Dongyue Wang, Kyo Kageura2026
18. Survey on Document-level Neural Machine Translation (文档级神经机器翻译综述)Journal of SoftwareXinglin Lv (吕星林), Junhui Li (李军辉), Shimin Tao (陶仕敏), et al.2024

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