Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
<|reference_start|>Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels: 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.<|reference_end|>
@article{jia2024combining,
    title={Combining Supervised Learning and Reinforcement Learning for Multi-Label
  Classification Tasks with Partial Labels},
    author={Zixia Jia, Junpeng Li, Shichuan Zhang, Anji Liu, Zilong Zheng},
    journal={arXiv preprint arXiv:2406.16293},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16293},
    primaryClass={cs.CL cs.AI}
}