GTM: A Generative Triple-Wise Model for Conversational Question Generation
This addresses the challenge of avoiding dull or deviated questions in conversational AI, improving human-machine interactions, though it is incremental by building on prior work that uses answer information.
The paper tackled the problem of generating coherent and diverse questions in open-domain conversations by proposing a generative triple-wise model with hierarchical variations, which improved question quality in terms of fluency, coherence, and diversity over baselines on a large-scale dataset.
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.