@article{TACL1171,
	author = {Mrkšić, Nikola  and  Vulić, Ivan  and  Ó Séaghdha, Diarmuid  and  Leviant, Ira  and  Reichart, Roi  and  Gašić, Milica  and  Korhonen, Anna  and  Young, Steve},
	title = {Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {5},
	year = {2017},
	keywords = {},
	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.},
	issn = {2307-387X},
	url = {https://transacl.org/ojs/index.php/tacl/article/view/1171},
	pages = {309--324}
}