  While state-of-the-art NLP models have demonstrated excellent performance for
aspect based sentiment analysis (ABSA), substantial evidence has been presented
on their lack of robustness. This is especially manifested as significant
degradation in performance when faced with out-of-distribution data. Recent
solutions that rely on counterfactually augmented datasets show promising
results, but they are inherently limited because of the lack of access to
explicit causal structure. In this paper, we present an alternative approach
that relies on non-counterfactual data augmentation. Our proposal instead
relies on using noisy, cost-efficient data augmentations that preserve
semantics associated with the target aspect. Our approach then relies on
modelling invariances between different versions of the data to improve
robustness. A comprehensive suite of experiments shows that our proposal
significantly improves upon strong pre-trained baselines on both standard and
robustness-specific datasets. Our approach further establishes a new
state-of-the-art on the ABSA robustness benchmark and transfers well across
domains.
