Transition-based Opinion Generation for Aspect-based Sentiment Analysis

Tianlai Ma, Zhongqing Wang, Guodong Zhou

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 propose a transition system for opinion tree generation, designed to better exploit pre-trained language models for structured fine-tuning. Our proposed transition system ensures the structural integrity of the generated opinion tree. By leveraging pre-trained generation models and simplifying the transition set, we are able to maximize the accuracy of opinion tree generation. 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.
Anthology ID:
2024.findings-acl.182
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3078–3087
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.182/
DOI:
10.18653/v1/2024.findings-acl.182
Bibkey:
Cite (ACL):
Tianlai Ma, Zhongqing Wang, and Guodong Zhou. 2024. Transition-based Opinion Generation for Aspect-based Sentiment Analysis. In Findings of the Association for Computational Linguistics: ACL 2024, pages 3078–3087, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Transition-based Opinion Generation for Aspect-based Sentiment Analysis (Ma et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.182.pdf
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