Abstract
Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selection strategies do not control for total training data, confounding language selection effects with data quantity effects. We introduce Budget-Xfer, a framework that formulates multi-source cross-lingual transfer as a budget-constrained resource allocation problem. Given a fixed annotation budget B, our framework jointly optimizes which source languages to include and how much data to allocate from each. We evaluate four allocation strategies across named entity recognition and sentiment analysis for three African target languages (Hausa, Yoruba, Swahili) using two multilingual models, conducting 288 experiments. Our results show that (1) multi-source transfer significantly outperforms single-source transfer (Cohen's d = 0.80 to 1.98), driven by a structural budget underutilization bottleneck; (2) among multi-source strategies, differences are modest and non-significant; and (3) the value of embedding similarity as a selection proxy is task-dependent, with random selection outperforming similarity-based selection for NER but not sentiment analysis.
Abstract (translated)
跨语言迁移学习通过利用高资源语言的标注数据，使低资源语言的自然语言处理成为可能。然而，现有关于源语言选择策略的比较研究未控制总训练数据量，导致语言选择效应与数据量效应相互混淆。我们提出了Budget-Xfer框架，该框架将多源跨语言迁移形式化为预算约束下的资源分配问题。在固定标注预算B的条件下，我们的框架联合优化应纳入的源语言及从每种语言分配的数据量。我们使用两种多语言模型，在三个非洲目标语言（豪萨语、约鲁巴语、斯瓦希里语）的命名实体识别和情感分析任务上评估了四种分配策略，共开展288项实验。结果表明：（1）多源迁移显著优于单源迁移（Cohen's d = 0.80至1.98），其驱动力来自结构性预算利用不足的瓶颈；（2）在多源策略中，差异较小且不显著；（3）嵌入相似性作为选择代理的价值具有任务依赖性——在命名实体识别中随机选择优于基于相似性的选择，但在情感分析中并非如此。
URL
https://arxiv.org/abs/2603.27651
