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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
%A Vulić, Ivan
%A Mrkšić, Nikola
%A Reichart, Roi
%A Ó Séaghdha, Diarmuid
%A Young, Steve
%A Korhonen, Anna
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F vulic-etal-2017-morph
%X 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.
%R 10.18653/v1/P17-1006
%U https://aclanthology.org/P17-1006
%U https://doi.org/10.18653/v1/P17-1006
%P 56-68

Markdown (Informal)

[Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules](https://aclanthology.org/P17-1006) (Vulić et al., ACL 2017)

• Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules (Vulić et al., ACL 2017)

ACL

• Ivan Vulić, Nikola Mrkšić, Roi Reichart, Diarmuid Ó Séaghdha, Steve Young, and Anna Korhonen. 2017. Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 56–68, Vancouver, Canada. Association for Computational Linguistics.