Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering

• URL: http://arxiv.org/abs/2109.07095v1
• Date: Wed, 15 Sep 2021 05:53:40 GMT
• Title: Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering
• Authors: Zhe Lin, Yitao Cai and Xiaojun Wan
• Abstract summary: We propose CoRPG (Coherence Relationship guided Paraphrase Generation) for document-level paraphrase generation. We use graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence. Our model can generate document paraphrase with more diversity and semantic preservation.
• Score: 88.08581016329398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper, we explore the task of document-level paraphrase generation for the first time and focus on the inter-sentence diversity by considering sentence rewriting and reordering. We propose CoRPG (Coherence Relationship guided Paraphrase Generation), which leverages graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence, which can be used for re-arranging the multiple (possibly modified) input sentences. We create a pseudo document-level paraphrase dataset for training CoRPG. Automatic evaluation results show CoRPG outperforms several strong baseline models on the BERTScore and diversity scores. Human evaluation also shows our model can generate document paraphrase with more diversity and semantic preservation.

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