Abstract:Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pretrained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embe… Show more
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