MixQG: Neural Question Generation with Mixed Answer Types
This work addresses the problem of generating diverse question types for AI systems, though it is incremental as it builds on existing neural approaches by expanding answer type coverage.
The paper tackles the limitation of existing neural question generation methods that focus on short factoid answers by introducing MixQG, a model trained on diverse answer types, which outperforms prior work in both seen and unseen domains and generates questions at different cognitive levels.
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.