{"type":"ScholarlyArticle","title":"Weak Supervision Enhanced Generative Network for Question Generation","authors":[{"name":"Yutong Wang","slug":"yutong-wang","given":"Yutong","family":"Wang","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Jiyuan Zheng","slug":"jiyuan-zheng","given":"Jiyuan","family":"Zheng","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Qijiong Liu","slug":"qijiong-liu","given":"Qijiong","family":"Liu","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Zhou Zhao","slug":"zhou-zhao","given":"Zhou","family":"Zhao","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Jun Xiao","slug":"jun-xiao","given":"Jun","family":"Xiao","affiliation":"College of Computer Science and Technology , Zhejiang University , China"},{"name":"Yueting Zhuang","slug":"yueting-zhuang","given":"Yueting","family":"Zhuang","affiliation":"College of Computer Science and Technology , Zhejiang University , China"}],"abstract":"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.","doi":"10.24963/ijcai.2019/528","url":"https://ijcai.org/proceedings/2019/528","pdf":"https://ijcai.org/proceedings/2019/0528.pdf","markdown":"https://ijcai.org/proceedings/2019/528.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/528","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":528,"pages":{"first":3806,"last":3812},"keywords":["Natural Language Processing: Natural Language Generation","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Dauphin et al., 2017] Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. 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