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arXiv · 2104.12567

Evaluating the Values of Sources in Transfer Learning

Md Rizwan Parvez·Kai-Wei Chang
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Abstract

Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP). However, when training a transfer model over multiple sources, not every source is equally useful for the target. To better transfer a model, it is essential to understand the values of the sources. In this paper, we develop SEAL-Shap, an efficient source valuation framework for quantifying the usefulness of the sources (e.g., domains/languages) in transfer learning based on the Shapley value method. Experiments and comprehensive analyses on both cross-domain and cross-lingual transfers demonstrate that our framework is not only effective in choosing useful transfer sources but also the source values match the intuitive source-target similarity.

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Md Rizwan Parvez, Kai-Wei Chang. 2021-04-26. Evaluating the Values of Sources in Transfer Learning. https://arxiv.org/abs/2104.12567

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