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Papers/Hierarchical Text Classification with Reinforced Label Ass...

Hierarchical Text Classification with Reinforced Label Assignment

Yuning Mao, Jingjing Tian, Jiawei Han, Xiang Ren

2019-08-27IJCNLP 2019 11Text ClassificationReinforcement Learningtext-classificationGeneral ClassificationClassification
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Text ClassificationRCV1Macro F160.1HiLAP (bow-CNN)
Text ClassificationRCV1Micro F183.3HiLAP (bow-CNN)
ClassificationRCV1Macro F160.1HiLAP (bow-CNN)
ClassificationRCV1Micro F183.3HiLAP (bow-CNN)

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