Span-based syntactic feature fusion for aspect sentiment triplet extraction

Abstract

Aspect sentiment triplet extraction (ASTE) is a particularly challenging subtask in aspect-based sentiment analysis. The span-based method is currently one of the mainstream solutions in this area. However, existing span-based methods focus only on semantic information, neglecting syntactic information, which has been proven effective in aspect-based sentiment classification. In this work, we combine syntactic information with the span-based method according to task characteristics and propose a span-based syntactic feature fusion (SSFF) model for ASTE. Firstly, we introduce part-of-speech information to assist span category prediction. Secondly, we introduce dependency distance information to assist sentiment polarity category prediction. By introducing the aforementioned syntactic information, the learning objectives of the first and second stages of the span-based method are clearly distinguished, which effectively improves the performance of the span-based method. We conduct experiments on the widely used public dataset ASTE-V2. The experimental results demonstrate that SSFF significantly improves the performance of the span-based method and outperforms all baseline models, achieving new state-of-the-art performance.