Multilingual Training of Crosslingual Word Embeddings

Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, Trevor Cohn


Abstract
Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer. Most prior work constructs embeddings for a pair of languages, with English on one side. We investigate methods for building high quality crosslingual word embeddings for many languages in a unified vector space. In this way, we can exploit and combine strength of many languages. We obtained high performance on bilingual lexicon induction, monolingual similarity and crosslingual document classification tasks.
Anthology ID:
E17-1084
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
894–904
Language:
URL:
https://aclanthology.org/E17-1084
DOI:
Bibkey:
Cite (ACL):
Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, and Trevor Cohn. 2017. Multilingual Training of Crosslingual Word Embeddings. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 894–904, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Multilingual Training of Crosslingual Word Embeddings (Duong et al., EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1084.pdf
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