  Cross-domain sentiment classification has drawn much attention in recent
years. Most existing approaches focus on learning domain-invariant
representations in both the source and target domains, while few of them pay
attention to the domain-specific information. Despite the non-transferability
of the domain-specific information, simultaneously learning domain-dependent
representations can facilitate the learning of domain-invariant
representations. In this paper, we focus on aspect-level cross-domain sentiment
classification, and propose to distill the domain-invariant sentiment features
with the help of an orthogonal domain-dependent task, i.e. aspect detection,
which is built on the aspects varying widely in different domains. We conduct
extensive experiments on three public datasets and the experimental results
demonstrate the effectiveness of our method.
