Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

Simon Suster, Ivan Titov, Gertjan van Noord

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

Abstract

We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chosen sense. The two components are estimated jointly. We observe that the word representations induced from bilingual data outperform the monolingual counterparts across a range of evaluation tasks, even though crosslingual information is not available at test time.
Original languageEnglish
Title of host publicationProceedings of NAACL-HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, June 12-17, 2016
Place of PublicationSan Diego, California, USA
PublisherAssociation for Computational Linguistics
Pages1346-1356
Number of pages11
ISBN (Print)978-1-941643-91-4
DOIs
Publication statusPublished - Jun 2016
Event15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - San Diego, United States
Duration: 12 Jun 201617 Jun 2016
http://naacl.org/naacl-hlt-2016/
http://naacl.org/naacl-hlt-2016/

Conference

Conference15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2016
CountryUnited States
CitySan Diego
Period12/06/1617/06/16
Internet address

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