Learning to translate in real-time with neural machine translation

Jiatao Gu, Graham Neubig, Kyunghyun Cho, Victor O.K. Li

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

Abstract

Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.

Original languageEnglish (US)
Title of host publicationLong Papers - Continued
PublisherAssociation for Computational Linguistics (ACL)
Pages1053-1062
Number of pages10
ISBN (Electronic)9781510838604
DOIs
StatePublished - 2017
Event15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
Duration: Apr 3 2017Apr 7 2017

Publication series

Name15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Volume1

Other

Other15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
CountrySpain
CityValencia
Period4/3/174/7/17

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

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