Aspect Information Enhanced Contrastive Learning for Aspect-based Sentiment Analysis
Authors: Yanjiang Chen, Kai Zhang, Zhe Yang, xinjie Sun, Kun Zhang, Qi Liu


Reviewer paper summaries: This paper presents a novel method called Aspect Information Enhanced Contrastive Learning Learning (AIECL) to address the scarcity of context for specific aspects and interference from irrelevant words in sentences in ABSA. The method uses advanced prompting techniques with LLMs to generate detailed aspect-specific descriptions, enhancing aspect-related context. It introduces a fusion module to integrate aspect insights with the original sentence structure and develops three contrastive learning strategies to explore complex correlations between samples. Experiments show that AIECL can outperform current SOTA models.
Summary of strengths: (1) This article successfully utilizes LLMs to enhance performance in ABSA tasks, which is one of the current trend in handling tasks in this field.

(2) This paper employs three semantics-based contrastive learning losses to capture correlations between samples, which is a relatively new approach and xperiments demonstrate its effectiveness.
Summary of weaknesses: (1) The example provided in Figure 1 is not convincing enough. Irrelevant contexts for specific aspects are a common issue in ABSA. A more effective illustration would be to show scenarios that other works fail to address.

(2) This article only introduces how their method is designed but does not clearly explain why it is designed this way. The explanation of insights in the methodology is insufficient.

(3) Just as the authors mention in the Limitations section, I am curious about the extent to which the quality of sentences generated by LLMs influences the performance of the proposed model. In other words, if the LLM is switched from Chatgpt to GPT-4 or Llama, etc, will the model's performance be significantly affected?
Comments, suggestions and typos: Section 3 Methodology can be reorganized. As it stands, it only shows how each module works, but the overall workflow is not explained very clearly.
Overall assessment scores: 2