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.

翻译：领域适应已被广泛用于跨域情感分析，以将知识从源域迁移至目标域。然而，现有方法大多假设目标（测试）域已知，导致其在实践中无法对未知测试数据取得良好泛化效果。本文聚焦跨域情感分析的领域泛化问题，具体提出一种基于后门调整的因果模型，以解构在应对领域偏移中起关键作用的领域特定表示与领域不变表示。首先，我们从因果视角重新审视跨域情感分析任务，建模不同变量间的因果关系；然后，通过后门调整消除领域混杂因素（如领域知识）的影响，学习不变特征表示。在多个同源与异源数据集上的系列实验表明，与当前最先进的领域泛化基线方法相比，本模型展现出优异的性能与鲁棒性。