Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng · 2024
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 positiveunlabelled 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 documentlevel relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework.