Challenges in Context-Aware Neural Machine Translation
Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma
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.
BibTeX
@inproceedings{
jin2023challenges,
title={Challenges in Context-Aware Neural Machine Translation},
author={Linghao Jin and Jacqueline He and Jonathan May and Xuezhe Ma},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=2anfut5geh}
}