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Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules

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Vulic, I 
Mrkšic, N 
Reichart, R 
Séaghdha, D 


Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that inexpensive is a rephrasing for expensive or may not associate acquire with acquires. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.



Semantic specialisation, Morphologically complex languages, Vector space models, Dialogue state tracking, Word embeddings

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ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

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Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Association for Computational Linguistics
European Research Council (648909)