通过图路由渐进微调，高效选择多源域最佳训练顺序。
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
- 将多源域建模为加权无向图，按路径优化训练顺序。
- 在自然语言推理任务上比当前最优提升2.3%，情感分析提升3.9%。
- 轻量级策略适合大规模多源域适配，计算开销低。
多源无监督域适应旨在利用多个源域的标注数据训练模型，使其在无标签的目标域上表现良好。源域选择对模型性能至关重要，依赖于源域与目标域之间的相似性。然而，现有方法在处理大量源域时往往需要复杂的计算过程来筛选最优源域。本文提出一种渐进微调（GFT）框架，将多个源域表示为无向加权图，并给出任意路径上的泛化误差界，用于确定最优训练路径。基于此，设计三种轻量级图路由策略以最小化误差界。最佳策略在自然语言推理（NLI）任务上较当前最优提升2.3%，在情感分析（SA）任务上表现优异，尤其在更多样化的数据子集上提升3.9%。
原文摘要 · Abstract (English)
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.
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