An attentional model for speech translation without transcription

Long Duong, Antonios Anastasopoulos, David Chiang, Steven Bird, Trevor Cohn

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

For many low-resource languages, spoken language resources are more likely to be annotated with translations than transcriptions. This bilingual speech data can be used for word-spotting, spoken document retrieval, and even for documentation of endangered languages. We experiment with the neural, attentional model applied to this data. On phoneto-word alignment and translation reranking tasks, we achieve large improvements relative to several baselines. On the more challenging speech-to-word alignment task, our model nearly matches GIZA++'s performance on gold transcriptions, but without recourse to transcriptions or to a lexicon.

Original languageEnglish
Title of host publication2016 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
EditorsKevin Knight, Ani Nenkova, Owen Rambow
Place of PublicationPensylvannia
PublisherAssociation for Computational Linguistics (ACL)
Pages949-959
Number of pages11
Volume1
ISBN (Electronic)9781941643914
DOIs
Publication statusPublished - Jun 2016
Externally publishedYes
Event15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - San Diego, United States
Duration: 12 Jun 201617 Jun 2016

Conference

Conference15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
CountryUnited States
CitySan Diego
Period12/06/1617/06/16

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