OT-Class: Optimal Transport-Enhanced Multi-label Text Classification
Authors: Daniel Hajialigol, Xuan Wang


Reviewer paper summaries: This paper introduces a novel multi-label text classification framework called OT-CLASS, which enhances word-to-label alignment using Optimal Transport (OT) to improve classification performance. OT-CLASS employs a multi-task learning strategy, with document classification as the primary task and identifying corresponding labels for words in the input document via OT as an auxiliary task. Experimental results demonstrate that OT-CLASS outperforms baselines that do not utilize word-to-label alignment, particularly in fine-grained label spaces.
Summary of strengths: 1. The introduction of OT for word-to-label alignment is a novel approach, addressing the gap in existing work that has not fully leveraged this alignment information.
2. By simultaneously performing document classification and word-to-label alignment, the model's classification capability is enhanced, especially in fine-grained label spaces.
3. The experimental results show that OT-CLASS significantly outperforms baselines that do not use word-to-label alignment, demonstrating its effectiveness.
Summary of weaknesses: 1. The introduction of OT may increase the computational complexity of the model. This could lead to longer training times, especially when dealing with large-scale datasets.
2. Although OT-CLASS uses an unsupervised method for word-to-label alignment, it still requires a significant amount of data for training and validation. Acquiring and processing this data could be challenging.
3. While OT can provide alignment information between words and labels, the interpretability of these alignments may not be as straightforward as other explicit methods. Additional analytical tools might be needed to understand the model's decision-making process.
Comments, suggestions and typos: n/a
Overall assessment scores: 1.5