Bilingual lexicon induction by learning to combine word-level and character-level representations
Accepted version
Peer-reviewed
Repository URI
Repository DOI
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Authors
Heyman, G
Vulíc, I
Moens, MF
Abstract
We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.
Description
Keywords
Bilingual lexicon induction, Cross-lingual NLP, Character-level and recurrent models, Terminology extraction, Multilinguality
Journal Title
15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
Conference Name
Proceedings of the 15th Conference of the European Chapter of the
Association for Computational Linguistics: Volume 1, Long Papers
Journal ISSN
Volume Title
2
Publisher
Association for Computational Linguistics
Publisher DOI
Sponsorship
European Research Council (648909)