Answer-guided and semantic coherent question generation in open-domain conversation

Abstract

Generating intriguing question is a key step towards building human-like open-domain chat-bots. Although some recent works have focused on this task, compared with questions raised by humans, significant gaps remain in maintaining semantic coherence with the posts, which may result in generating dull or deviated questions. We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation. Thus, we devise two methods to further enhance semantic coherence between post and question under the guidance of answer. First, the coherence score between generated question and answer is used as the reward function in a reinforcement learning framework, to encourage the cases that are consistent with the answer in semantic. Second, we incorporate adversarial training to explicitly control question generation in the direction of question-answer coherence. The extensive experiments show that our two methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.