The University of Edinburgh-Uppsala University’s Submission to the WMT 2020 Chat Translation Task

Nikita Moghe, Christian Hardmeier, Rachel Bawden

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes the joint submission of the University of Edinburgh and Uppsala University to the WMT'20 chat translation task for both language directions (English↔German). We use existing state-of-the-art machine translation models trained on news data and fine-tune them on in-domain and pseudo-in-domain web crawled data. We also experiment with (i) adaptation using speaker and domain tags and (ii) using different types and amounts of preceding context. We observe that contrarily to expectations, exploiting context degrades the results (and on analysis the data is not highly contextual). However using domain tags does improve scores according to the automatic evaluation. Our final primary systems use domain tags and are ensembles of 4 models, with noisy channel reranking of outputs. Our en-de system was ranked second in the shared task while our de-en system outperformed all the other systems.
Original languageEnglish
Title of host publicationProceedings of the Fifth Conference on Machine Translation
PublisherAssociation for Computational Linguistics (ACL)
Pages473-478
Number of pages6
ISBN (Print)978-1-948087-81-0
Publication statusPublished - 19 Nov 2020
EventFifth Conference on Machine Translation - Online Conference
Duration: 19 Nov 202020 Nov 2020
http://www.statmt.org/wmt20/

Conference

ConferenceFifth Conference on Machine Translation
Abbreviated titleWMT 2020
CityOnline Conference
Period19/11/2020/11/20
Internet address

Keywords

  • machine translation
  • dialogue
  • chat translation
  • German
  • English
  • shared task

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