A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis
Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, Dacheng Tao
Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is always sensitive to the multi-aspect challenge, where features of multiple aspects in a sentence will affect each other. To mitigate this issue, we design a novel training framework, called Contrastive Cross-Channel Data Augmentation (C3 DA), which leverages an in-domain generator to construct more multi-aspect samples and then boosts the robustness of ABSA models via contrastive learning on these generated data. In practice, given a generative pretrained language model and some limited ABSA labeled data, we first employ some parameter-efficient approaches to perform the in-domain fine-tuning. Then, the obtained in-domain generator is used to generate the synthetic sentences from two channels, i.e., Aspect Augmentation Channel and Polarity Augmentation Channel, which generate the sentence condition on a given aspect and polarity respectively. Specifically, our C3 DA performs the sentence generation in a cross-channel manner to obtain more sentences, and proposes an Entropy-Minimization Filter to filter low-quality generated samples. Extensive experiments show that our C3 DA can outperform those baselines without any augmentations by about 1% on accuracy and Macro- F1. Code and data are released in https://github.com/wangbing1416/C3DA.- Anthology ID:
- 2022.coling-1.581
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6691–6704
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.581
- DOI:
- Cite (ACL):
- Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, and Dacheng Tao. 2022. A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6691–6704, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A Contrastive Cross-Channel Data Augmentation Framework for Aspect-Based Sentiment Analysis (Wang et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.coling-1.581.pdf
- Code
- wangbing1416/c3da