@inproceedings{yu-jiang-2021-expanding,
title = "Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates",
author = "Yu, Xiaojing and
Jiang, Anxiao",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.279",
doi = "10.18653/v1/2021.eacl-main.279",
pages = "3202--3212",
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.",
}
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%0 Conference Proceedings
%T Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates
%A Yu, Xiaojing
%A Jiang, Anxiao
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F yu-jiang-2021-expanding
%X 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.
%R 10.18653/v1/2021.eacl-main.279
%U https://aclanthology.org/2021.eacl-main.279
%U https://doi.org/10.18653/v1/2021.eacl-main.279
%P 3202-3212
Markdown (Informal)
[Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates](https://aclanthology.org/2021.eacl-main.279) (Yu & Jiang, EACL 2021)
ACL