Context-aware neural machine translation with mini-batch embedding

Makoto Morishita, Jun Suzuki, Tomoharu Iwata, Masaaki Nagata

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

Abstract

It is crucial to provide an inter-sentence context in Neural Machine Translation (NMT) models for higher-quality translation. With the aim of using a simple approach to incorporate inter-sentence information, we propose mini-batch embedding (MBE) as a way to represent the features of sentences in a mini-batch. We construct a mini-batch by choosing sentences from the same document, and thus the MBE is expected to have contextual information across sentences. Here, we incorporate MBE in an NMT model, and our experiments show that the proposed method consistently outperforms the translation capabilities of strong baselines and improves writing style or terminology to fit the document's context.

Original languageEnglish
Title of host publicationEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages2513-2521
Number of pages9
ISBN (Electronic)9781954085022
Publication statusPublished - 2021
Event16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online
Duration: 2021 Apr 192021 Apr 23

Publication series

NameEACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
CityVirtual, Online
Period21/4/1921/4/23

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Linguistics and Language

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