Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs

• URL: http://arxiv.org/abs/2110.13231v1
• Date: Mon, 25 Oct 2021 19:33:38 GMT
• Title: Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
• Authors: Monisha Jegadeesan, Sachin Kumar, John Wieting, Yulia Tsvetkov
• Abstract summary: We present a novel technique for zero-shot paraphrase generation. Key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora.
• Score: 28.16894664889912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into "meaning spaces" -- replacing the final softmax layer with word embeddings. This architectural modification, plus a training procedure that incorporates an autoencoding objective, enables effective parameter sharing across languages for more fluent monolingual rewriting, and facilitates fluency and diversity in generation. Our continuous-output paraphrase generation models outperform zero-shot paraphrasing baselines when evaluated on two languages using a battery of computational metrics as well as in human assessment.

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