Challenges in Context-Aware Neural Machine Translation

Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma

Submission Type: Regular Long Paper

Submission Track: Machine Translation

Keywords: neural machine translation, document-level neural machine translation, context-aware neural machine translation

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.

Submission Number: 2580