RulePG: Syntactic Rule-enhanced Paraphrase Generation

The deep neural network (DNN) has become prevailing for paraphrase generation (PG), a widely studied task in natural language processing. Even though the DNN models have shown impressive performances, they are still far from generating desired paraphrases that require sufficient diversity in wording while keeping the correct semantics. Meanwhile, the large DNN models suffer from significant cost of training, the difficulty of reproducibility, and the black-box training process, and thus make it hard to generalize and implement a novel model in other works. Therefore, we propose a simple but effective alternative: a post-hoc method called RulePG, which is based on syntactic rules for PG. It has the advantages of improving wording diversities while preserving semantics, and is designed post-hoc manner so that it is model-agnostic and would not require extra training when utilized. Moreover, instructed by the rules, the generation process is more interpretable with explicit control of sentence forms. To gain and make full use of rules, we construct SRPG (syntactic rules for paraphrase generation), the first syntactic rule set for PG. We novelly build semantic representations for SRPG and endow the RulePG with the capacity of semantic understanding. Automatic evaluation on various metrics and human evaluations demonstrate the effectiveness of our method and show that the RulePG reaches new state-of-the-art performances.