Recent advances in dialogue machine translation

Siyou Liu, Yuqi Sun, Longyue Wang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Recent years have seen a surge of interest in dialogue translation, which is a significant application task for machine translation (MT) technology. However, this has so far not been exten-sively explored due to its inherent characteristics including data limitation, discourse properties and personality traits. In this article, we give the first comprehensive review of dialogue MT, including well-defined problems (e.g., 4 perspectives), collected resources (e.g., 5 language pairs and 4 sub-domains), representative approaches (e.g., architecture, discourse phenomena and personality) and useful applications (e.g., hotel-booking chat system). After systematical investigation, we also build a state-of-the-art dialogue NMT system by leveraging a breadth of established approaches such as novel architectures, popular pre-training and advanced techniques. Encouragingly, we push the state-of-the-art performance up to 62.7 BLEU points on a commonly-used benchmark by using mBART pre-training. We hope that this survey paper could significantly promote the research in dialogue MT.

Original languageEnglish
Article number484
JournalInformation (Switzerland)
Issue number11
Publication statusPublished - Nov 2021


  • Benchmark data
  • Building advanced system
  • Dialogue
  • Discourse issue
  • Existing ap-proaches
  • Neural machine translation
  • Real-life applications


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