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
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6691–6704
Language:
URL:
https://aclanthology.org/2022.coling-1.581
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.581.pdf
Code
 wangbing1416/c3da