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MixQG: Neural Question Generation with Mixed Answer Types

Lidiya Murakhovs’ka, Chien-Sheng Wu, Philippe Laban, Tong Niu, Wenhao Liu, Caiming Xiong


Abstract
Asking good questions is an essential ability for both human and machine intelligence. However, existing neural question generation approaches mainly focus on short factoid type of answers. In this paper, we introduce a neural question generator, MixQG, to bridge this gap. We combine nine question answering datasets with diverse answer types, including yes/no, multiple-choice, extractive, and abstractive answers, to train a single generative model. We show with empirical results that our model outperforms existing work in both seen and unseen domains, and can generate questions with different cognitive levels when conditioned on different answer types. We run a human evaluation study to assess the quality of generated questions and find that MixQG outperforms the next best model by 10%. Our code and model checkpoints will be released and integrated with the HuggingFace library to facilitate various downstream applications.
Anthology ID:
2022.findings-naacl.111
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1486–1497
Language:
URL:
https://aclanthology.org/2022.findings-naacl.111
DOI:
10.18653/v1/2022.findings-naacl.111
Bibkey:
Cite (ACL):
Lidiya Murakhovs’ka, Chien-Sheng Wu, Philippe Laban, Tong Niu, Wenhao Liu, and Caiming Xiong. 2022. MixQG: Neural Question Generation with Mixed Answer Types. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1486–1497, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
MixQG: Neural Question Generation with Mixed Answer Types (Murakhovs’ka et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.111.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.111.mp4
Code
 salesforce/qgen
Data
BoolQDROPMCTestNarrativeQANatural QuestionsQuorefSQuAD