MixQG: Neural Question Generation with Mixed Answer Types

• URL: http://arxiv.org/abs/2110.08175v1
• Date: Fri, 15 Oct 2021 16:03:40 GMT
• Title: MixQG: Neural Question Generation with Mixed Answer Types
• Authors: Lidiya Murakhovs'ka, Chien-Sheng Wu, Tong Niu, Wenhao Liu, Caiming Xiong
• Abstract summary: We propose a neural question generator, MixQG, to bridge this gap. We combine 9 question answering datasets with diverse answer types, including yes/no, multiple-choice, extractive, and abstractive answers. Our model outperforms existing work in both seen and unseen domains.
• Score: 54.23205265351248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asking good questions is an essential ability for both human and machine intelligence. However, existing neural question generation approaches mainly focus on the short factoid type of answers. In this paper, we propose a neural question generator, MixQG, to bridge this gap. We combine 9 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. Our code is released and well-integrated with the Huggingface library to facilitate various downstream applications.

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