Johannes Bjerva
Center for Language and Cognition Groningen, University of Groningen, The Netherlands
Robert Ö stling
Department of Linguistics, Stockholm University, Sweden
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Published in: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden
Linköping Electronic Conference Proceedings 131:24, p. 211-215
NEALT Proceedings Series 29:24, p. 211-215
Published: 2017-05-08
ISBN: 978-91-7685-601-7
ISSN: 1650-3686 (print), 1650-3740 (online)
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.
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