Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning

TL;DR

This paper introduces a minimalist tagging scheme combined with contrastive learning for Aspect Sentiment Triplet Extraction, achieving high performance with less complexity and outperforming large language models in few-shot scenarios.

Contribution

It proposes a novel, simplified tagging scheme and contrastive learning method that improve ASTE efficiency and effectiveness over existing complex approaches.

Findings

Comparable or superior performance to state-of-the-art methods

Reduced computational overhead and model complexity

Outperforms GPT 3.5 and GPT 4 in few-shot learning scenarios

Abstract

Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive learning approach to mitigate these challenges. The proposed approach demonstrates comparable or superior performance in comparison to state-of-the-art techniques, while featuring a more compact design and reduced computational overhead. Notably, even in the era of Large Language Models (LLMs), our method exhibits superior efficacy compared to GPT 3.5 and GPT 4 in a few-shot learning scenarios. This study also provides valuable insights for the advancement of ASTE techniques within the paradigm of large language models.