Enhancing Paraphrase Question Generation With Prior Knowledge

Jiayuan Xie, Wenhao Fang, Qingbao Huang, Yi Cai, Tao Wang

Abstract: Paraphrase question generation (PQG) aims to rewrite a given original question to a new paraphrase question, where the paraphrase question needs to have the same expressed meaning as the original question, but have a difference in expression form. Existing methods on PQG mainly focus on synonym substitution or word order adjustment based on the original question. However, rewriting based on the word-level may not guarantee the difference between paraphrase questions and original questions. In this paper, we propose a knowledge-aware paraphrase question generation model. Our model first employs a knowledge extractor to extract the prior knowledge related to the original question from the knowledge base. Then an attention mechanism and a gate mechanism are introduced in our model to selectively utilize the extracted prior knowledge for rewriting, which helps to expand the content of the generated question to maximize the difference. Additionally, we use a discriminator module to promote the generated paraphrase to be semantically close to the original question and the ground truth. Specifically, the loss function of the discriminator penalizes the excessive distance between the representation of the paraphrase question and the ground truth. Extensive experiments on the Quora dataset show that the proposed model outperforms the baselines. Further, our model is applied to the SQuAD dataset, which proves the generalization ability of our model in the existing QA dataset.