  Aspect-based sentiment classification (ABSC) is a very challenging subtask of
sentiment analysis (SA) and suffers badly from the class-imbalance. Existing
methods only process sentences independently, without considering the
domain-level relationship between sentences, and fail to provide effective
solutions to the problem of class-imbalance. From an intuitive point of view,
sentences in the same domain often have high-level semantic connections. The
interaction of their high-level semantic features can force the model to
produce better semantic representations, and find the similarities and nuances
between sentences better. Driven by this idea, we propose a plug-and-play
Pairwise Semantic Interaction (PSI) module, which takes pairwise sentences as
input, and obtains interactive information by learning the semantic vectors of
the two sentences. Subsequently, different gates are generated to effectively
highlight the key semantic features of each sentence. Finally, the adversarial
interaction between the vectors is used to make the semantic representation of
two sentences more distinguishable. Experimental results on four ABSC datasets
show that, in most cases, PSI is superior to many competitive state-of-the-art
baselines and can significantly alleviate the problem of class-imbalance.
