Evaluating the Values of Sources in Transfer Learning
Md Rizwan Parvez, Kai-Wei Chang · 2021
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.