Overcoming the curse of sentence length for neural machine translation using automatic segmentation

Jean Pouget-Abadie, Dzmitry Bahdanau, Bart van Merriënboer, Kyunghyun Cho, Yoshua Bengio

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

Abstract

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.

Original languageEnglish (US)
Title of host publicationProceedings of SSST 2014 - 8th Workshop on Syntax, Semantics and Structure in Statistical Translation
EditorsDekai Wu, Marine Carpuat, Xavier Carreras, Eva Maria Vecchi
PublisherAssociation for Computational Linguistics (ACL)
Pages78-85
Number of pages8
ISBN (Electronic)9781937284961
StatePublished - 2014
Event8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014 - Doha, Qatar
Duration: Oct 25 2014 → …

Publication series

NameProceedings of SSST 2014 - 8th Workshop on Syntax, Semantics and Structure in Statistical Translation

Conference

Conference8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014
Country/TerritoryQatar
CityDoha
Period10/25/14 → …

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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