Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels

• URL: http://arxiv.org/abs/2406.16293v1
• Date: Mon, 24 Jun 2024 03:36:19 GMT
• Title: Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
• Authors: Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng,
• Abstract summary: We propose an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, demonstrate the generalization and effectiveness of our framework.
• Score: 27.53399899573121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose challenges in fully manual annotation due to the specific domain knowledge and large class sets. Therefore, we address the multi-label positive-unlabelled learning (MLPUL) problem, where only a subset of positive classes is annotated. We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework.

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