  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.
