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Retrofitting is a technique used to move word vectors closer together orfurther 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 aone-to-one mapping between concepts and their retrofitted counterparts. Itapplies that mapping (post-specializes) to handle concepts that do not appearin the original KB in a manner similar to how some natural language systemshandle out-of-vocabulary entries. We test our system on three word-similaritybenchmarks and a downstream sentence simplification task and achieve the stateof the art (CARD-660). Altogether, our results demonstrate our system'seffectiveness for out-of-knowledge and rare word generalization.

RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations