Multi-label document classification (MLDC) aims to allocate more than one label to each document and attracts increasing attention in many practical applications. However, previous studies have failed to pay sufficient attention to the lack of semantic information on labels and the long-tail problem prevalent in the datasets. Additionally, most existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. In this paper, we propose a simple and effective paradigm for MLDC. Regarding the problem of insufficient label information and imbalance in the sample size of categories, we utilize large language models (LLMs) to semantically expand the label content and generate pseudo-samples for the tail categories. To optimize the features of both documents and labels, we design the contrastive learning boosted feature optimization module facilitated by the similarity matrices. Finally, we construct a label-guided feature selection module to incorporate the optimized label features into the input features to provide richer semantic information for the classifier. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art baselines.

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as dialogue policy learning (DPL) or response generation (RG), while neglecting dialogue state tracking (DST) for understanding. This narrow focus limits the systems to achieve globally optimal performance by overlooking the interdependence between understanding and generation. Additionally, RL methods face challenges with sparse and delayed rewards, which complicates training and optimization. To address these issues, we extend RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. The understanding reward increases as more slots are correctly filled in DST, while the generation reward grows with the accurate inclusion of user requests. Our approach provides a balanced optimization aligned with task completion. Experimental results demonstrate that our approach effectively enhances the performance of TOD systems and achieves new state-of-the-art results on three widely used datasets, including MultiWOZ2.0, MultiWOZ2.1, and In-Car. Our approach also shows superior few-shot ability in low-resource settings compared to current models.