MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
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.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: Sentiment Analysis, NLP Applications
Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency
Languages Studied: English
Submission Number: 5611
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