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arXiv · 2406.16293

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

Zixia Jia·Junpeng Li·Shichuan Zhang·Anji Liu·Zilong Zheng
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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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Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng. 2024-06-24. Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels. https://arxiv.org/abs/2406.16293

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