LAKA: A Label-Aware and Knowledge-Augmented Framework for Multi-Label Text Classification
Authors: Lianxi Wang, Huayu Huang, Zhuowei Chen, Wenxiu Xie


Reviewer paper summaries: The paper proposes an LAKA framework for Multi-Label Text Classification (MLTC). This framework enhances interactions between labels and text by incorporating external knowledge and label-aware embeddings. Specifically, the LAKA framework comprises two main components: Multi-Label Text Knowledge Augmentation (MTKA) and Label-Aware Union Embeddings (LAUE). Experimental results demonstrate that LAKA significantly outperforms six mainstream baseline models across three datasets.
Summary of strengths: 1. The proposed LAKA framework is innovative in combining external knowledge and label-aware embeddings to enhance label-text interactions, significantly improving the performance of multi-label text classification. This approach is a novel contribution to current MLTC research.
2. The paper addresses the complex relationships between labels and text by enhancing label-text interactions through LAUE and leveraging external knowledge via MTKA using a Graph Neural Network (GNN). This comprehensive enhancement contributes to the improved performance of the model.
3. The authors conducted extensive experiments on three datasets, demonstrating the superior performance of the LAKA framework. The comparison with six mainstream baseline models highlights the effectiveness and robustness of the proposed method.
4. The design of the LAKA framework is modular, making it easy to integrate with existing Pre-trained Language Models (PLMs). This modularity ensures good scalability and applicability to other similar tasks.
Summary of weaknesses: 1. Although the LAKA framework significantly improves performance, its implementation complexity is high. Introducing GNN and External Knowledge Fusion (EKF) layers increases computational costs and implementation difficulty in practical applications.
2. The effectiveness of the proposed method relies on the quality and availability of external knowledge. The inaccurate or difficult to obtain external knowledge may affect the model's final performance.
3. While the authors conducted experiments on three datasets, the representativeness of these datasets needs further validation. The model's performance may vary across different domain-specific datasets.
4. The paper primarily demonstrates the method's superiority through quantitative experiments but lacks qualitative analysis of specific examples. In-depth analysis of particular instances would help better understand the model's behavior, strengths, and weaknesses.
Comments, suggestions and typos: see Weaknesses
Overall assessment scores: 2.5