Understanding Different Output Layers in Label-Wise Attention Networks for Multi-Label Text Classification
Authors: Dongli He, Chih-Jen Lin


Reviewer paper summaries: This paper studies the effects of different output layers in Label-wise Attention Networks (LWANs). While LWANs have achieved success in extreme multi-label text classification (EMTC) tasks, there is a lack of guidance on which output layer is best suited for specific scenarios. Researchers often rely on intuition or convention rather than empirical evidence to select output layers. Therefore, it is necessary to conduct an in-depth study of the properties of these output layers.
Summary of strengths: The paper explores a very practical problem in this field, namely, how to choose the output layer more reasonably. This is a very useful topic for EMTC in this domain.
Summary of weaknesses: 1. The experimental analysis is not thorough enough. For example, in Section 5.1 "Understand the Behaviors of Different Output Layers," the authors only describe the basic phenomena observed in the experiments without providing in-depth analysis or conclusions. This does not achieve the claimed goal of "understanding the behaviors of different output layers."

2. The contribution of the paper is insufficient. The final experimental conclusion is "recommend TLP for RNN-based models, while lwSLP is suggested for BERT-based models. There is no definitive suggestion for CNN-based models." This may not be universally applicable and lacks deeper analysis. This greatly limits the contribution of the paper.
Comments, suggestions and typos: The topic is crucial, but it requires thorough analysis.
Overall assessment scores: 2