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.

我们提出了基于强化学习的 RLAC 框架，通过结合强化学习的探索能力和监督学习的开发能力，解决了多标签正负样本学习问题，并在多个任务中进行实验证明了我们框架的泛化和有效性。