Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure

Joseph Peper, Lu Wang


Abstract
Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction. First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encouraging the model to produce input representations that are discriminable across key input attributes, such as sentiment polarity and the existence of implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured generation format that better adapts pre-trained autoregressive encoder-decoder models to extract quadruples in a generative fashion. Experimental results show that GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging 1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset. Additionally, we see significant gains on implicit aspect and opinion splits that have been shown as challenging for existing ACOS approaches.
Anthology ID:
2022.findings-emnlp.451
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6089–6095
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.451
DOI:
10.18653/v1/2022.findings-emnlp.451
Bibkey:
Cite (ACL):
Joseph Peper and Lu Wang. 2022. Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6089–6095, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure (Peper & Wang, Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.451.pdf