  Aspect sentiment classification (ASC) aims at determining sentiments
expressed towards different aspects in a sentence. While state-of-the-art ASC
models have achieved remarkable performance, they are recently shown to suffer
from the issue of robustness. Particularly in two common scenarios: when
domains of test and training data are different (out-of-domain scenario) or
test data is adversarially perturbed (adversarial scenario), ASC models may
attend to irrelevant words and neglect opinion expressions that truly describe
diverse aspects. To tackle the challenge, in this paper, we hypothesize that
position bias (i.e., the words closer to a concerning aspect would carry a
higher degree of importance) is crucial for building more robust ASC models by
reducing the probability of mis-attending. Accordingly, we propose two
mechanisms for capturing position bias, namely position-biased weight and
position-biased dropout, which can be flexibly injected into existing models to
enhance representations for classification. Experiments conducted on
out-of-domain and adversarial datasets demonstrate that our proposed approaches
largely improve the robustness and effectiveness of current models.
