TitleA Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
AuthorsYang, Pengcheng
Luo, Fuli
Ma, Shuming
Lin, Junyang
Sun, Xu
AffiliationPeking Univ, Deep Learning Lab, Beijing Inst Big Data Res, Beijing, Peoples R China
Peking Univ, Sch EECS, MOE Key Lab Computat Linguist, Beijing, Peoples R China
Issue Date2019
Publisher57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019)
AbstractMulti-label classification (MLC) aims to predict a set of labels for a given instance. Based on a pre-defined label order, the sequence-to-sequence (Seq2Seq) model trained via maximum likelihood estimation method has been successfully applied to the MLC task and shows powerful ability to capture high-order correlations between labels. However, the output labels are essentially an unordered set rather than an ordered sequence. This inconsistency tends to result in some intractable problems, e.g., sensitivity to the label order. To remedy this, we propose a simple but effective sequence-to-set model. The proposed model is trained via reinforcement learning, where reward feedback is designed to be independent of the label order. In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels. Extensive experiments show that our approach can substantially outperform competitive baselines, as well as effectively reduce the sensitivity to the label order.(1)
URIhttp://hdl.handle.net/20.500.11897/552802
IndexedISSHP
CPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

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