Evaluating the Values of Sources in Transfer Learning

Md Rizwan Parvez

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