Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning

arXiv:2403.07342v1 Announce Type: new
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 …

abstract analysis arxiv cs.ai cs.cl data external data extract extraction fine-grained novel research sentiment sentiment analysis tagging textual type unstructured