Weak Supervision Enhanced Generative Network for Question Generation

Weak Supervision Enhanced Generative Network for Question Generation

Yutong Wang, Jiyuan Zheng, Qijiong Liu, Zhou Zhao, Jun Xiao, Yueting Zhuang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 3806-3812. https://doi.org/10.24963/ijcai.2019/528

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Machine Learning: Deep Learning
Natural Language Processing: Natural Language Generation