Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis
Abstract: Aspect-based sentiment analysis (ABSA) assesses sentiments towards specific aspects within texts, resulting in detailed sentiment tuples.
Previous ABSA models often use static templates to predict all of the elements in the tuples, and these models often fail to accurately capture dependencies between elements.
Multi-view prompting method improves the performance of ABSA by predicting tuples with various templates and then ensembling 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 generates necessary views for each instance based on instance-level entropy.
Ensuring the diverse and relevant view generation, our proposed method improves F1-scores on ASQP and ACOS datasets while significantly reducing inference time.
Paper Type: Short
Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Research Area Keywords: stance detection; applications
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 5612
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