Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, Steve Young

Abstract
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.
Anthology ID:
Q17-1022
Volume:
Transactions of the Association for Computational Linguistics, Volume 5
Month:
Year:
2017
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Kristina Toutanova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
309–324
Language:
URL:
https://aclanthology.org/Q17-1022
DOI:
10.1162/tacl_a_00063
Bibkey:
Cite (ACL):
Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, Ira Leviant, Roi Reichart, Milica Gašić, Anna Korhonen, and Steve Young. 2017. Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints. Transactions of the Association for Computational Linguistics, 5:309–324.
Cite (Informal):
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints (Mrkšić et al., TACL 2017)
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https://preview.aclanthology.org/emnlp22-frontmatter/Q17-1022.pdf
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