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Hits 1 – 4 of 4
1
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
Mrkšić, Nikola
;
Vulić, Ivan
;
Séaghdha, Diarmuid Ó
. - : arXiv, 2017
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2
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ...
Vulić, Ivan
;
Mrkšić, Nikola
;
Reichart, Roi
. - : arXiv, 2017
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3
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
Mrkšić, Nikola
;
Vulić, Ivan
;
Ó Séaghdha, Diarmuid
. - : Apollo - University of Cambridge Repository, 2017
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4
Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Mrkšić, Nikola
;
Vulić, Ivan
;
Ó Séaghdha, Diarmuid
;
Leviant, Ira
;
Reichart, Roi
;
Gašić, Milica
;
Korhonen, Anna
;
Young, Steve
. - : Association for Computational Linguistics, 2017. : https://www.transacl.org/ojs/index.php/tacl/article/view/1171, 2017. : Transactions of the Association for Computational Linguistics (TACL), 2017
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 specialised 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-specialised 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. ; Ivan Vulic, Roi Reichart and Anna Korhonen are supported by the ERC Consolidator Grant LEXICAL (number 648909). Roi Reichart is also supported by the Intel-ICRI grant: Hybrid Models for Minimally Supervised Information Extraction from Conversations.
URL:
https://doi.org/10.17863/CAM.10174
https://www.repository.cam.ac.uk/handle/1810/266633
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