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Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysistask, which focuses on detecting the sentiment polarity towards the aspect in asentence. However, it is always sensitive to the multi-aspect challenge, wherefeatures of multiple aspects in a sentence will affect each other. To mitigatethis issue, we design a novel training framework, called ContrastiveCross-Channel Data Augmentation (C3 DA), which leverages an in-domain generatorto construct more multi-aspect samples and then boosts the robustness of ABSAmodels via contrastive learning on these generated data. In practice, given agenerative pretrained language model and some limited ABSA labeled data, wefirst employ some parameter-efficient approaches to perform the in-domainfine-tuning. Then, the obtained in-domain generator is used to generate thesynthetic sentences from two channels, i.e., Aspect Augmentation Channel andPolarity Augmentation Channel, which generate the sentence condition on a givenaspect and polarity respectively. Specifically, our C3 DA performs the sentencegeneration in a cross-channel manner to obtain more sentences, and proposes anEntropy-Minimization Filter to filter low-quality generated samples. Extensiveexperiments show that our C3 DA can outperform those baselines without anyaugmentations by about 1% on accuracy and Macro- F1. Code and data are releasedin https://github.com/wangbing1416/C3DA.

A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis