MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu · 2024
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 stateof-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 fewshot 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.