Translation invariant word embeddings

Matt Gardner, Kejun Huang, Evangelos Papalexakis, Xiao Fu, Partha Talukdar, Christos Faloutsos, Nicholas Sidiropoulos, Tom Mitchell

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

16 Scopus citations


This work focuses on the task of finding latent vector representations of the words in a corpus. In particular, we address the issue of what to do when there are multiple languages in the corpus. Prior work has, among other techniques, used canonical correlation analysis to project pre-trained vectors in two languages into a common space. We propose a simple and scalable method that is inspired by the notion that the learned vector representations should be invariant to translation between languages. We show empirically that our method outperforms prior work on multilingual tasks, matches the performance of prior work on monolingual tasks, and scales linearly with the size of the input data (and thus the number of languages being embedded).

Original languageEnglish (US)
Title of host publicationConference Proceedings - EMNLP 2015
Subtitle of host publicationConference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics (ACL)
Number of pages5
ISBN (Electronic)9781941643327
StatePublished - 2015
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal
Duration: Sep 17 2015Sep 21 2015

Publication series

NameConference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing


OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2015

Bibliographical note

Publisher Copyright:
© 2015 Association for Computational Linguistics.


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