  Retrofitting is a technique used to move word vectors closer together or
further apart in their space to reflect their relationships in a Knowledge Base
(KB). However, retrofitting only works on concepts that are present in that KB.
RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a
one-to-one mapping between concepts and their retrofitted counterparts. It
applies that mapping (post-specializes) to handle concepts that do not appear
in the original KB in a manner similar to how some natural language systems
handle out-of-vocabulary entries. We test our system on three word-similarity
benchmarks and a downstream sentence simplification task and achieve the state
of the art (CARD-660). Altogether, our results demonstrate our system's
effectiveness for out-of-knowledge and rare word generalization.
