Diversity-enhanced Learning for Unsupervised Syntactically Controlled Paraphrase Generation

Anonymous

Abstract: Syntactically controlled paraphrase generation is to generate diverse sentences that have the same semantics as the given original sentence but conform to the target syntactic structure. An optimal opportunity to enhance diversity is to make word substitutions during rephrasing based on syntactic control. Existing unsupervised methods have made great progress in syntactic control, but the generated paraphrases rarely have substitutions due to the limitation of training data. In this paper, we propose a Diversity syntactically controlled Paraphrase generation framework (DiPara), in which a novel training strategy is designed to obtain semantic sentences as semantic sentences while using the given sentence as training objects. As diverse words vary the syntactic structure around them, we propose a phrase-aware attention mechanism to capture the syntactic structure associated with the current word. To achieve it, the linearized triple sequence is introduced to represent structure singly. Experiment results on two datasets show that DiPara outperforms strong baselines in generating diverse paraphrases with target syntax.

Paper Type: long

Research Area: Syntax: Tagging, Chunking and Parsing / ML

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English