M ini C on GTS : A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction (2024)

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to co-extract the sentiment triplets in a given corpus. Existing approaches within the pretraining-finetuning paradigm tend to either meticulously craft complex tagging schemes and classification heads, or incorporate external semantic augmentation to enhance performance. In this study, we, for the first time, re-evaluate the redundancy in tagging schemes and the internal enhancement in pretrained representations. We propose a method to improve and utilize pretrained representations by integrating a minimalist tagging scheme and a novel token-level contrastive learning strategy. The proposed approach demonstrates comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead. Additionally, we are the first to formally evaluate GPT-4’s performance in few-shot learning and Chain-of-Thought scenarios for this task. The results demonstrate that the pretraining-finetuning paradigm remains highly effective even in the era of large language models.

## introduction aspect-based sentiment analysis (absa) aims to jointly extract opinion terms, aspect terms (targets of the corresponding opinions), and their specific sentiment polarities in a given corpus. in the milestone research by @xcite , the compound absa subtasks were consolidated into the aspect sentiment triplet extraction (aste) task framework. for each input corpus, aste outputs triplets in the form (aspect, opinion, polarity), where the aspect term is the target or entity being discussed, the opinion term is the sentiment or opinion expressed about the aspect, and polarity indicates whether the opinion is positive, * corresponding authors negative, or neutral. figure 1 illustrates the aste task. ## conclusion in this work, we have introduced an elegant and efficient framework for aste, achieving sota performance. our approach is built upon two effective components: a new tagging scheme and a novel token-level contrastive learning implementation. the ablation study demonstrates the synergy between these components, reducing the need for complex model designs and external information enhancements.