While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4% in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin. Data and code can be found at https://github.com/morningmoni/HiLAP.
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system
School of Software, South China Normal University（南方科技大学软件学院）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；University of Electronic Science and Technology of China（电子科技大学）；University of Toronto（多伦多大学）；University of Connecticut（康涅狄格大学）；AI center, AIGCode Inc.（AIGCode公司人工智能中心）
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Institute for Language, Cognition and Computation, University of Edinburgh（爱丁堡大学语言、认知与计算研究所）；Usher Institute, University of Edinburgh（爱丁堡大学乌谢尔研究所）；School of Literatures, Languages and Cultures, University of Edinburgh（爱丁堡大学文学、语言与文化学院）
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；University of Central Florida（中佛罗里达大学）；University of Science and Technology of China（中国科学技术大学）；University of Macau（澳门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
