Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels. Source domain selection plays a crucial role in determining the model's performance. It relies on the similarities amongst source and target domains. Nonetheless, existing work for source domain selection often involves heavyweight computational procedures, especially when dealing with numerous source domains and the need to identify the best ones from them. In this paper, we introduce a framework for gradual fine tuning (GFT) of machine learning models on multiple source domains. We represent multiple source domains as an undirected weighted graph. We then give a new generalization error bound for GFT along any path within the graph, which is used to determine the optimal path corresponding to the optimal training order. With this formulation, we introduce three lightweight graph-routing strategies which tend to minimize the error bound. Our best strategy improves $2.3\%$ of accuracy over the state-of-the-art on Natural Language Inference (NLI) task and achieves competitive performance on Sentiment Analysis (SA) task, especially a $3.9\%$ improvement on a more diverse subset of data we use for SA.

本研究解决了多源无监督领域适应中源域选择的计算复杂性问题。通过将多个源域表示为无向带权图，提出了一种渐进微调框架，并引入了三种轻量级图路由策略，以最小化泛化误差界限。结果显示，该方法在自然语言推断任务上相较于最先进技术提高了2.3%的准确率，并在情感分析任务上尤其在更具多样性的数据子集上提高了3.9%。

基于图路由的多源无监督领域适应的渐进微调