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

2021-06-07 14:07:07
Lei Shen, Fandong Meng, Jinchao Zhang, Yang Feng, Jie Zhou

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.

Abstract (translated)

URL

https://arxiv.org/abs/2106.03635

PDF

https://arxiv.org/pdf/2106.03635.pdf


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