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In this work, we present a novel counter-fitting method which injectsantonymy and synonymy constraints into vector space representations in order toimprove the vectors' capability for judging semantic similarity. Applying thismethod to publicly available pre-trained word vectors leads to a new state ofthe art performance on the SimLex-999 dataset. We also show how the method canbe used to tailor the word vector space for the downstream task of dialoguestate tracking, resulting in robust improvements across different dialoguedomains.

Counter-fitting Word Vectors to Linguistic Constraints