PMN: A Prototype Network Based Metric Framework for Solving Aspect-Based Sentiment Analysis Tasks
Authors: Chenhao Li, Jun Long


Reviewer paper summaries: This paper proposes the PMN framework to solve the few-shot Aspect Sentiment Quad Prediction problem in a unified way through adopting a prototypical network. This framework can train multiple asop tasks simultaneously with task transferability. Extensive experiments show that the framework exhibits robust performance.
Summary of strengths: This paper explains the relative aspect sentiment related problems unified in the PMN framework. The paper is easy to follow logically, with well-illustrated figures and tables to explain what are the related acop elements adopted in each task and how are each task related and how is information transferred through different tasks. The comparison experiments show that PMN achieves SOTA result on both QUAD and ACOS datasets.
Summary of weaknesses: 1.	The paper appears to lack substantial innovation in its framework structure, as the proposed PMN framework closely resembles the conventional prototype learning framework within the context of few-shot learning (FSL). Consequently, its novelty appears limited.
2.	The experiments are not convincing enough. Firstly, the exclusion of 2024 models for comparison on the QUAD and ACOS datasets undermines the credibility of the results. Secondly, given the framework's intended application to few-shot aspect sentiment classification, the absence of comparative experiments under few-shot settings constitutes a notable oversight. While the authors demonstrate the framework's effectiveness through F1 scores across tasks, a comparative analysis against other few-shot models or direct utilization of large language models is imperative to comprehensively evaluate its performance under few-shot settings.
3.	This paper uses too much space to introduce background knowledge in different sections. In the introduction section, information for different aspect sentiment tasks can be greatly reduced. Also in section 3.2 Baseline, introduction for comparison methods can also be shortened.
Comments, suggestions and typos: 1.	Paragraph 2 of Introduction can be shortened, the background knowledge is a bit excessive. 
2.	Grammar mistake in Section 2.1 Paragraph 3, “Given a training setwe sample multiple sets”
3.	I think the author should use special fonts when self-defined words such as ‘acop’, ‘ac’ and ‘op’ are mentioned, or it will be a bit confusing for readers.
Overall assessment scores: 2.5