  Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental
task in opinion mining that aims to extract the targets (or aspects) on which
opinions have been expressed. Recent work focus on cross-domain OTE, which is
typically encountered in real-world scenarios, where the testing and training
distributions differ. Most methods use domain adversarial neural networks that
aim to reduce the domain gap between the labelled source and unlabelled target
domains to improve target domain performance. However, this approach only
aligns feature distributions and does not account for class-wise feature
alignment, leading to suboptimal results. Semi-supervised learning (SSL) has
been explored as a solution, but is limited by the quality of pseudo-labels
generated by the model. Inspired by the theoretical foundations in domain
adaptation [2], we propose a new SSL approach that opts for selecting target
samples whose model output from a domain-specific teacher and student network
disagree on the unlabelled target data, in an effort to boost the target domain
performance. Extensive experiments on benchmark cross-domain OTE datasets show
that this approach is effective and performs consistently well in settings with
large domain shifts.
