Domain Generalization via Causal Adjustment for Cross-Domain Sentiment
  Analysis
Siyin Wang Jie Zhou Qin Chen Qi Zhang Tao Gui Xuanjing Huang
Domain adaption has been widely adapted for cross-domain sentiment analysis
to transfer knowledge from the source domain to the target domain. Whereas,
most methods are proposed under the assumption that the target (test) domain is
known, making them fail to generalize well on unknown test data that is not
always available in practice. In this paper, we focus on the problem of domain
generalization for cross-domain sentiment analysis. Specifically, we propose a
backdoor adjustment-based causal model to disentangle the domain-specific and
domain-invariant representations that play essential roles in tackling domain
shift. First, we rethink the cross-domain sentiment analysis task in a causal
view to model the causal-and-effect relationships among different variables.
Then, to learn an invariant feature representation, we remove the effect of
domain confounders (e.g., domain knowledge) using the backdoor adjustment. A
series of experiments over many homologous and diverse datasets show the great
performance and robustness of our model by comparing it with the
state-of-the-art domain generalization baselines.