A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis

Siyu Wang, Jianhui Jiang, Shengran Dai, Jiangtao Qiu

Abstract
Aspect category sentiment analysis (ACSA) aims to simultaneously detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs). Some recent studies have used pre-trained generative models to complete ACSA and achieved good results. However, for ACSA, generative models still face three challenges. First, addressing the missing predictions in ACSA is crucial, which involves accurately predicting all category-sentiment pairs within a sentence. Second, category-sentiment pairs are inherently a disordered set. Consequently, the model incurs a penalty even when its predictions are correct, but the predicted order is inconsistent with the ground truths. Third, different aspect categories should focus on relevant sentiment words, and the polarity of the aspect category should be the aggregation of the polarities of these sentiment words. This paper proposes a hierarchical generative model with a coverage mechanism using sequence-to-set learning to tackle all three challenges simultaneously. Our model’s superior performance is demonstrated through extensive experiments conducted on several datasets.
Anthology ID:
2024.lrec-main.54
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
626–635
Language:
URL:
https://aclanthology.org/2024.lrec-main.54
DOI:
Bibkey:
Cite (ACL):
Siyu Wang, Jianhui Jiang, Shengran Dai, and Jiangtao Qiu. 2024. A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 626–635, Torino, Italia. ELRA and ICCL.
Cite (Informal):
A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis (Wang et al., LREC-COLING 2024)
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