Don't until the final verb wait: Reinforcement learning for simultaneous machine translation

Alvin C. Grissom, Jordan Boyd-Graber, He He, John Morgan, Hal Daumé

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

Abstract

We introduce a reinforcement learningbased approach to simultaneous machine translation-producing a translation while receiving input words- between languages with drastically different word orders: from verb-final languages (e.g., German) to verb-medial languages (English). In traditional machine translation, a translator must "wait" for source material to appear before translation begins. We remove this bottleneck by predicting the final verb in advance. We use reinforcement learning to learn when to trust predictions about unseen, future portions of the sentence. We also introduce an evaluation metric to measure expeditiousness and quality. We show that our new translation model outperforms batch and monotone translation strategies.

Original languageEnglish (US)
Title of host publicationEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1342-1352
Number of pages11
ISBN (Electronic)9781937284961
StatePublished - 2014
Event2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014 - Doha, Qatar
Duration: Oct 25 2014Oct 29 2014

Publication series

NameEMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014
Country/TerritoryQatar
CityDoha
Period10/25/1410/29/14

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Information Systems

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