  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.
