  Aspect-based sentiment analysis (ABSA) aims at automatically inferring the
specific sentiment polarities toward certain aspects of products or services
behind the social media texts or reviews, which has been a fundamental
application to the real-world society. Since the early 2010s, ABSA has achieved
extraordinarily high accuracy with various deep neural models. However,
existing ABSA models with strong in-house performances may fail to generalize
to some challenging cases where the contexts are variable, i.e., low robustness
to real-world environments. In this study, we propose to enhance the ABSA
robustness by systematically rethinking the bottlenecks from all possible
angles, including model, data, and training. First, we strengthen the current
best-robust syntax-aware models by further incorporating the rich external
syntactic dependencies and the labels with aspect simultaneously with a
universal-syntax graph convolutional network. In the corpus perspective, we
propose to automatically induce high-quality synthetic training data with
various types, allowing models to learn sufficient inductive bias for better
robustness. Last, we based on the rich pseudo data perform adversarial training
to enhance the resistance to the context perturbation and meanwhile employ
contrastive learning to reinforce the representations of instances with
contrastive sentiments. Extensive robustness evaluations are conducted. The
results demonstrate that our enhanced syntax-aware model achieves better
robustness performances than all the state-of-the-art baselines. By
additionally incorporating our synthetic corpus, the robust testing results are
pushed with around 10% accuracy, which are then further improved by installing
the advanced training strategies. In-depth analyses are presented for revealing
the factors influencing the ABSA robustness.
