A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators
Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, Xuanjing Huang
Abstract
Visual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from other language generation tasks. We argue that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written. Inspired by the setting of discriminator in adversarial learning, we propose two discriminators, one for each attribute, to enhance the training. We then use the reinforcement learning framework to incorporate scores from the two discriminators as the reward to guide the training of the question generator. Experimental results on a benchmark VQG dataset show the effectiveness and robustness of our model compared to some state-of-the-art models in terms of both automatic and human evaluation metrics.- Anthology ID:
- C18-1150
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1763–1774
- Language:
- URL:
- https://aclanthology.org/C18-1150
- DOI:
- Cite (ACL):
- Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, and Xuanjing Huang. 2018. A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1763–1774, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (Fan et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/C18-1150.pdf
- Data
- MS COCO, VQG, Visual Question Answering