Counter-fitting Word Vectors to Linguistic Constraints
  In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to publicly available pre-trained word vectors leads to a new state of the art performance on the SimLex-999 dataset. We also show how the method can be used to tailor the word vector space for the downstream task of dialogue state tracking, resulting in robust improvements across different dialogue domains. 
Nikola Mrk\v{s}i\'c and Diarmuid \'O S\'eaghdha and Blaise Thomson and   Milica Ga\v{s}i\'c and Lina Rojas-Barahona and Pei-...
cs.CL cs.LG