Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers significant advantages over single-domain learning. However, the limited availability of annotated data in each domain considerably hinders the effectiveness of conventional supervised MDL approaches in real-world applications. In this paper, we introduce a novel method called multi-domain contrastive learning (MDCL) to alleviate the impact of insufficient annotations by capturing both semantic and structural information from both labeled and unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic alignment and intra-domain contrast. The former aims to align annotated instances of the same semantic category from distinct domains within a shared hidden space, while the latter focuses on learning a cluster structure of unlabeled instances in a private hidden space for each domain. MDCL is readily compatible with many SP models, requiring no additional model parameters and allowing for end-to-end training. Experimental results across five textual and image multi-domain datasets demonstrate that MDCL brings noticeable improvement over various SP models.Furthermore, MDCL can further be employed in multi-domain active learning (MDAL) to achieve a superior initialization, eventually leading to better overall performance.

翻译：多领域学习（MDL）指同时利用不同领域收集的数据集构建一个或多个模型。传统方法遵循共享-私有框架（SP模型），强调领域共享信息的提取与领域私有信息的保留，相比单领域学习具有显著优势。然而，实际应用中各领域标注数据的稀缺性严重制约了传统监督式MDL方法的有效性。本文提出一种名为多领域对比学习（MDCL）的新方法，通过从标注与非标注数据中同时捕获语义与结构信息，缓解标注不足的影响。具体而言，MDCL包含两个模块：跨领域语义对齐与领域内对比。前者旨在将不同领域中相同语义类别的标注实例对齐至共享隐空间，后者则专注于为每个领域在私有隐空间中学习非标注实例的聚类结构。MDCL可便捷地与多种SP模型兼容，无需额外模型参数且支持端到端训练。在五个文本与图像多领域数据集上的实验结果表明，MDCL能为各类SP模型带来显著性能提升。此外，MDCL还可应用于多领域主动学习（MDAL）实现更优的初始化，从而最终获得更好的整体性能。