CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation

Zichu Fei, Qi Zhang, Tao Gui, Di Liang, Sirui Wang, Wei Wu, Xuanjing Huang


Abstract
Multi-hop question generation focuses on generating complex questions that require reasoning over multiple pieces of information of the input passage. Current models with state-of-the-art performance have been able to generate the correct questions corresponding to the answers. However, most models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. To address this challenge, we propose the CQG, which is a simple and effective controlled framework. CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions. In addition, we introduce a novel controlled Transformer-based decoder to guarantee that key entities appear in the questions. Experiment results show that our model greatly improves performance, which also outperforms the state-of-the-art model about 25% by 5 BLEU points on HotpotQA.
Anthology ID:
2022.acl-long.475
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6896–6906
Language:
URL:
https://aclanthology.org/2022.acl-long.475
DOI:
10.18653/v1/2022.acl-long.475
Bibkey:
Cite (ACL):
Zichu Fei, Qi Zhang, Tao Gui, Di Liang, Sirui Wang, Wei Wu, and Xuanjing Huang. 2022. CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6896–6906, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (Fei et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.475.pdf
Code
 sion-zcfei/cqg
Data
HotpotQA