Counter-fitting Word Vectors to Linguistic Constraints

Understand

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.