Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates
Xiaojing Yu, Anxiao Andrew Jiang · 2021
Sequence-to-sequence based models have recently shown promising results in generating high-quality questions.However, these models are also known to have main drawbacks such as lack of diversity and bad sentence structures.In this paper, we focus on question generation over SQL database and propose a novel framework by expanding, retrieving, and infilling that first incorporates flexible templates with a neural-based model to generate diverse expressions of questions with guidance of sentence structure.Furthermore, a new activation/deactivation mechanism is proposed for template-based sequenceto-sequence generation, which learns to discriminate template patterns and content patterns, thus further improves generation quality.We conduct experiments on two largescale cross-domain datasets.The experiments show that the superiority of our question generation method in producing more diverse questions while maintaining high quality and consistency under both automatic evaluation and human evaluation.