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Simple and Effective Paraphrastic Similarity from Parallel Translations

Annual Meeting of the Association for Computational Linguistics (ACL), 2019
Abstract

We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora. Further, we show that the resulting model can be applied to cross-lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.

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