Contrastive variational information bottleneck for aspect-based
sentiment analysis
- URL: http://arxiv.org/abs/2303.02846v3
- Date: Thu, 21 Dec 2023 07:35:18 GMT
- Title: Contrastive variational information bottleneck for aspect-based
sentiment analysis
- Authors: Mingshan Chang, Min Yang, Qingshan Jiang, and Ruifeng Xu
- Abstract summary: We propose to reduce spurious correlations for aspect-based sentiment analysis (ABSA) via a novel Contrastive Variational Information Bottleneck framework (called CVIB)
The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning.
Our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization.
- Score: 36.83876224466177
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning techniques have dominated the literature on aspect-based
sentiment analysis (ABSA), achieving state-of-the-art performance. However,
deep models generally suffer from spurious correlations between input features
and output labels, which hurts the robustness and generalization capability by
a large margin. In this paper, we propose to reduce spurious correlations for
ABSA, via a novel Contrastive Variational Information Bottleneck framework
(called CVIB). The proposed CVIB framework is composed of an original network
and a self-pruned network, and these two networks are optimized simultaneously
via contrastive learning. Concretely, we employ the Variational Information
Bottleneck (VIB) principle to learn an informative and compressed network
(self-pruned network) from the original network, which discards the superfluous
patterns or spurious correlations between input features and prediction labels.
Then, self-pruning contrastive learning is devised to pull together
semantically similar positive pairs and push away dissimilar pairs, where the
representations of the anchor learned by the original and self-pruned networks
respectively are regarded as a positive pair while the representations of two
different sentences within a mini-batch are treated as a negative pair. To
verify the effectiveness of our CVIB method, we conduct extensive experiments
on five benchmark ABSA datasets and the experimental results show that our
approach achieves better performance than the strong competitors in terms of
overall prediction performance, robustness, and generalization. Code and data
to reproduce the results in this paper is available at:
https://github.com/shesshan/CVIB.
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