Challenges in Context-Aware Neural Machine Translation

• URL: http://arxiv.org/abs/2305.13751v2
• Date: Mon, 23 Oct 2023 21:01:26 GMT
• Title: Challenges in Context-Aware Neural Machine Translation
• Authors: Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma
• Abstract summary: Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve discourse dependencies. Despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. We propose a more realistic setting for document-level translation, called paragraph-to-paragraph (para2para) translation.
• Score: 39.89082986080746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and 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 several challenges that impede progress within this 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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