Transcription of Learning Structured Representation for Text Classification ...
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Learning Structured Representation for Text Classification ...
Learning Structured Representation for Text Classification via Reinforcement Learning Tianyang Zhang? , Minlie Huang?, , Li Zhao . ? Tsinghua National Laboratory for Information Science and Technology Dept. of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China . Microsoft Research Asia . Corresponding Author: (Minlie Huang). Abstract and recursive autoencoders (Socher et al. 2013; 2011; Qian et al. 2015) use pre-specified parsing trees to build Structured Representation Learning is a fundamental problem in natural representations. Attention-based methods (Yang et al. 2016;. language processing. This paper studies how to learn a struc- tured Representation for text Classification . Unlike most ex- Zhou, Wan, and Xiao 2016; Lin et al. 2017) use attention isting Representation models that either use no structure or mechanisms to build representations by scoring input words rely on pre-specified structures, we propose a reinforcemen- or sentences differentially.
been few studies on learning representations with automati-cally optimized structures. Yogatama et al. (2017) proposed to compose binary tree structure for sentence representa-tion with only supervision from downstream tasks, but such structure is very complex and overly deep, leading to un-satisfactory classification performance. In (Chung ...
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