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.

翻译：深度学习技术在基于方面的情感分析（ABSA）领域占据主导地位，取得了最先进的性能。然而，深度模型通常存在输入特征与输出标签之间的虚假相关性，这极大地损害了模型的鲁棒性和泛化能力。本文通过提出一种新颖的对比变分信息瓶颈框架（简称CVIB），旨在减少ABSA中的虚假相关性。所提出的CVIB框架由一个原始网络和一个自剪枝网络组成，这两个网络通过对比学习同步优化。具体而言，我们采用变分信息瓶颈（VIB）原则从原始网络中学习一个信息量丰富且压缩的网络（自剪枝网络），该网络丢弃了输入特征与预测标签之间的多余模式或虚假相关性。然后，我们设计了自剪枝对比学习，将语义相似的正样本对拉近，并将不相似的样本对推远，其中原始网络和自剪枝网络分别学习的锚点表示被视为正样本对，而一个小批次内两个不同句子的表示则被视为负样本对。为了验证我们CVIB方法的有效性，我们在五个基准ABSA数据集上进行了大量实验，实验结果表明，我们的方法在整体预测性能、鲁棒性和泛化能力方面均优于强大的竞争对手。重现本文结果的代码和数据可在以下链接获取：https://github.com/shesshan/CVIB。