Using Ordinal Labels for Text Augmentation and Simultaneous Contrastive Learning for DownStream Task.
Authors: Kibin Yang, Hayoung Oh


Reviewer paper summaries: This paper addresses the issue of how to generate augmented data using ordered labels and apply it to contrastive learning to improve model performance in NLP downstream tasks. Traditional methods struggle to effectively differentiate texts with subtle differences in text classification. By introducing ordered labels, the proposed method enhances the model's sensitivity to these subtle differences, thereby improving text classification accuracy.
Summary of strengths: The paper proposes a novel data augmentation method and a corresponding contrastive learning algorithm.
Summary of weaknesses: - The introduction lacks logical coherence. The paper lists some related work but does not describe the connection between the text and these works.
- The experiments are seriously insufficient. First, representative algorithms of today are not selected for comparison. Second, testing is conducted on only one dataset. Further in-depth research and experiments are recommended.
Comments, suggestions and typos: - The citation format in the paper is non-compliant and should be revised.
- The paper appears to be incomplete, especially in the experimental section. It is recommended to improve the completeness of the work.
Overall assessment scores: 2
Metareview score: 2
Metareview: This paper proposes a novel approach to enhance text classification by integrating contrastive learning with augmented data generated through prompt engineering using a large language model. Specifically, the authors use ChatGPT to create examples with ordinal labels, which represent different levels or degrees. These generated examples are employed as negative samples in contrastive learning, aiming to improve the model's ability to distinguish between subtly different texts.

The key claim is the simultaneous application of contrastive learning and traditional classification tasks. This method leverages ordered labels to refine the model's sensitivity to minor text differences, thereby boosting classification accuracy. By incorporating both contrastive and classification training, the proposed approach enhances data diversity and model performance, outperforming conventional fine-tuning methods that rely solely on text classification. The study's effectiveness is demonstrated using text reviews from a Korean shopping platform.
Summary of reasons to publish: The work suggests new ways to expand the available data for classification by using ordinal labels. This can provide better options for contrastive learning and also improve robustness for models if done well. This connects well with contrastive learning and may improve downstream task performance.