ERA: A Model-Agnostic Robustness-Aware Method for Aspect Sentiment Triplet Extraction
Authors: Shaopeng Tang, Lin Li, Xiaohui Tao, Leqi Zhong, Qing Xie


Reviewer paper summaries: This paper presents a model-agnostic method called ERA designed to enhance the robustness of Aspect Sentiment Triplet Extraction (ASTE) models. The study analyzes the weak robustness of most current SOTA ASTE models through metamorphic testing (MT). Additionally, it propose an element-wise robustness-aware loss which can be integrated into existing ASTE models via uncertainty weighing and improve their ability of generalization. Experiments show that ERA can significantly enhance the robustness of ASTE models.
Summary of strengths: (1) The observation proposed by this paper is interesting: The extraction/sentiment of the triplet can be inverted by a simple conversion.


(2) In addition to demonstrating the effectiveness of the proposed methods, this paper tests the efficacy of traditional metrics such as F1-score and violation rate.


(3) Extensive experiment results show that the proposed loss can enhance the robustness of current ASTE models and is model-agnostic.
Summary of weaknesses: (1)	 Though metamorphic testing is relatively new in the context of analyzing ASTE models, it is a well-established practice in software engineering. Furthermore, as demonstrated by the authors in Section 2.2, many studies have assessed the robustness of deep learning models using metamorphic testing. Consequently, the novelty of this part has somewhat declined.


(2)	 This paper propose a complicated method, however, there’s no ablation study verifying the necessity of each component.

(3)	 Considering the complexity of the model, experiments are needed to illustrate how much it slows down the base model in terms of time efficiency.
Comments, suggestions and typos: The overview of ERA (figure 2) can be aesthetically enhanced.
Overall assessment scores: 2.5