@inproceedings{jin-etal-2023-challenges,
title = "Challenges in Context-Aware Neural Machine Translation",
author = "Jin, Linghao and
He, Jacqueline and
May, Jonathan and
Ma, Xuezhe",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.943",
doi = "10.18653/v1/2023.emnlp-main.943",
pages = "15246--15263",
abstract = "Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.",
}
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<abstract>Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.</abstract>
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%0 Conference Proceedings
%T Challenges in Context-Aware Neural Machine Translation
%A Jin, Linghao
%A He, Jacqueline
%A May, Jonathan
%A Ma, Xuezhe
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jin-etal-2023-challenges
%X Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.
%R 10.18653/v1/2023.emnlp-main.943
%U https://aclanthology.org/2023.emnlp-main.943
%U https://doi.org/10.18653/v1/2023.emnlp-main.943
%P 15246-15263
Markdown (Informal)
[Challenges in Context-Aware Neural Machine Translation](https://aclanthology.org/2023.emnlp-main.943) (Jin et al., EMNLP 2023)
ACL
- Linghao Jin, Jacqueline He, Jonathan May, and Xuezhe Ma. 2023. Challenges in Context-Aware Neural Machine Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15246–15263, Singapore. Association for Computational Linguistics.