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Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations
Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.ORCID iD: 0000-0002-6027-4156
2017 (English)In: Proceedings of the 21st Nordic Conference on Computational Linguistics / [ed] Jörg Tiedemann, Linköping: Linköping University Electronic Press, 2017, p. 211-215, article id 024Conference paper, Published paper (Refereed)
Abstract [en]

Assessing the semantic similarity between sentences in different languages is challenging. We approach this problem by leveraging multilingual distributional word representations, where similar words in different languages are close to each other. The availability of parallel data allows us to train such representations on a large amount of languages. This allows us to leverage semantic similarity data for languages for which no such data exists. We train and evaluate on five language pairs, including English, Spanish, and Arabic. We are able to train wellperforming systems for several language pairs, without any labelled data for that language pair.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. p. 211-215, article id 024
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 131
Keywords [en]
word representations, multilingual NLP, semantic similarity estimation, natural language processing
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
Identifiers
URN: urn:nbn:se:su:diva-145545ISBN: 978-91-7685-601-7 (print)OAI: oai:DiVA.org:su-145545DiVA, id: diva2:1130215
Conference
21st Nordic Conference on Computational Linguistics, NoDaLiDa, Gothenburg, Sweden, 22-24 May 2017
Available from: 2017-08-08 Created: 2017-08-08 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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More languages
Output format
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