Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis

摘要

Aspect-based sentiment analysis (ABSA) assesses sentiments towards specific aspects within texts, resulting in detailed sentiment tuples.Previous ABSA models often used static templates to predict all the elements in the tuples, and these models often failed to accurately capture dependencies between elements. Multi-view prompting method improves the performance of ABSA by predicting tuples with various templates and then assembling the results. However, this method suffers from inefficiencies and out-of-distribution errors. In this paper, we propose a Dynamic Order Template (DOT) method for ABSA, which dynamically creates an order template that contains only the necessary views for each instance. Ensuring the diverse and relevant view generation, our proposed method improves F1 scores on ASQP and ACOS datasets while significantly reducing inference time.