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Post-specialisation: Retrofitting vectors of words unseen in lexical resources
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Discriminating between lexico-semantic relations with the specialization tensor model
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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Automatic Selection of Context Configurations for Improved Class-Specific Word Representations ...
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Cross-lingual syntactically informed distributed word representations ...
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Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2017
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
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Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ...
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Decoding Sentiment from Distributed Representations of Sentences ...
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Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ...
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ...
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Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules ...
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Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
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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
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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.
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URL: https://doi.org/10.17863/CAM.10174 https://www.repository.cam.ac.uk/handle/1810/266633
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Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules
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Vulic, Ivan; Mrkšic, N; Reichart, R. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2017
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Cross-lingual syntactically informed distributed word representations
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Vulic, Ivan. - : Association for Computational Linguistics, 2017. : 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 2017
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Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
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Rappoport, Ari; Reichart, Roi; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2017. : https://arxiv.org/pdf/1608.05528.pdf, 2017. : Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 2017
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If sentences could see: Investigating visual information for semantic textual similarity
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