GTM: A Generative Triple-Wise Model for Conversational Question Generation

• URL: http://arxiv.org/abs/2106.03635v1
• Date: Mon, 7 Jun 2021 14:07:07 GMT
• Title: GTM: A Generative Triple-Wise Model for Conversational Question Generation
• Authors: Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou
• Abstract summary: We propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG) Our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.
• Score: 36.33685095934868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To avoid dull or deviated questions, some researchers tried to utilize answer, the "future" information, to guide question generation. However, they separate a post-question-answer (PQA) triple into two parts: post-question (PQ) and question-answer (QA) pairs, which may hurt the overall coherence. Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations. To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs. Experimental results on a large-scale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.

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