Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates

Xiaojing Yu, Anxiao Jiang


Abstract
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 sentence structure guidance. Furthermore, a new activation/deactivation mechanism is proposed for template-based sequence-to-sequence generation, which learns to discriminate template patterns and content patterns, thus further improves generation quality. We conduct experiments on two large-scale 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.
Anthology ID:
2021.eacl-main.279
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3202–3212
Language:
URL:
https://aclanthology.org/2021.eacl-main.279
DOI:
10.18653/v1/2021.eacl-main.279
Bibkey:
Cite (ACL):
Xiaojing Yu and Anxiao Jiang. 2021. Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3202–3212, Online. Association for Computational Linguistics.
Cite (Informal):
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates (Yu & Jiang, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.279.pdf
Code
 xiaojingyu92/eriqg
Data
SPIDERWikiSQL