Transition-based Opinion Generation for Aspect-based Sentiment Analysis
Abstract: Recently, the use of pre-trained generation models for extracting sentiment elements has resulted in significant advancements in aspect-based sentiment analysis benchmarks. However, these approaches often overlook the importance of explicitly modeling structure among sentiment elements. To address this limitation, we present a study that aims to integrate general pre-trained sequence-to-sequence language models with a structure-aware transition-based approach.Therefore, we depart from a transition system for opinion tree generation, designed to better exploit pre-trained language models for structured fine-tuning. Extensive experiments show that our model significantly advances the state-of-the-art performance on several benchmark datasets. In addition, the empirical studies also indicate that the proposed opinion tree generation with transition system is more effective in capturing the sentiment structure than other generation models.
Paper Type: Long
Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Research Area Keywords: sentiment analysis, Aspect-based sentiment analysis
Contribution Types: NLP engineering experiment
Languages Studied: English
Section 2 Permission To Publish Peer Reviewers Content Agreement: Authors grant permission for ACL to publish peer reviewers' content
Submission Number: 50
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