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.

本文研究领域适应在跨领域情感分析中的应用，提出基于背景调整的因果模型来解决领域转移问题，并在多个数据集上与现有方法进行了比较。

通过因果调整实现跨域情感分析的领域泛化

Domain Generalization via Causal Adjustment for Cross-Domain Sentiment  Analysis

Cross-domain sentiment analysis aims to predict the sentiment of texts in the
target domain using the model trained on the source domain to cope with the
scarcity of labeled data. Previous studies are mostly cross-entropy-based
methods for the task, which suffer from instability and poor generalization. In
this paper, we explore contrastive learning on the cross-domain sentiment
analysis task. We propose a modified contrastive objective with in-batch
negative samples so that the sentence representations from the same class will
be pushed close while those from the different classes become further apart in
the latent space. Experiments on two widely used datasets show that our model
can achieve state-of-the-art performance in both cross-domain and multi-domain
sentiment analysis tasks. Meanwhile, visualizations demonstrate the
effectiveness of transferring knowledge learned in the source domain to the
target domain and the adversarial test verifies the robustness of our model.

本文探索对跨域情感分析任务采用对比学习方法，提出了一种修改后的对比目标，并在同一类的句子表示之间拉近，不同类的之间推远的方法，实验证明我们的模型在跨域和多域情感分析任务中都取得了最先进的性能，同时，可视化结果展示了知识转移的有效性，并通过对抗测试验证了模型的鲁棒性。

跨领域情感分析的纯对比学习

Mere Contrastive Learning for Cross-Domain Sentiment Analysis

Cross-domain sentiment analysis has received significant attention in recent
years, prompted by the need to combat the domain gap between different
applications that make use of sentiment analysis. In this paper, we take a
novel perspective on this task by exploring the role of external commonsense
knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet
knowledge graph to enrich the semantics of a document by providing both
domain-specific and domain-general background concepts. These concepts are
learned by training a graph convolutional autoencoder that leverages
inter-domain concepts in a domain-invariant manner. Conditioning a popular
domain-adversarial baseline method with these learned concepts helps improve
its performance over state-of-the-art approaches, demonstrating the efficacy of
our proposed framework.

本文提出了一种新的框架 KinGDOM，利用 ConceptNet 知识图谱中的通用知识和领域特定知识来丰富文档的语义，通过训练图卷积自编码器来学习交叉领域知识，同时利用外部常识知识来改进领域敌对基准方法的性能。它在交叉领域情感分析任务中展现出比其他最先进的方法更好的性能。