Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
This work addresses ASTE for fine-grained sentiment analysis, offering a more compact and efficient solution with incremental improvements.
The paper tackles Aspect Sentiment Triplet Extraction (ASTE) by proposing a minimalist tagging scheme with contrastive learning, achieving comparable or superior performance to state-of-the-art methods and outperforming GPT 3.5 and GPT 4 in few-shot scenarios.
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.