CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Conditional Generative Adversarial Networks for Commonsense Machine Comprehension
Wang Bingning1,2; Liu Kang1; Zhao Jun1,2
2017-08
Conference NameTwenty-Sixth International Joint Conference on Artificial Intelligence
Pages4123-4129
Conference Date2017-8-19
Conference PlaceAustralia
AbstractRecently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs (CGANs) in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.
KeywordGenerative Adversarial Networks Commonsense Reasoning Machine Comprehension
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20032
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
Wang Bingning,Liu Kang,Zhao Jun. Conditional Generative Adversarial Networks for Commonsense Machine Comprehension[C],2017:4123-4129.
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