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Title: Evaluating the Values of Sources in Transfer Learning
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 , an efficient source valuation framework for quantifying the usefulness of the sources (e.g., ) 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.  more » « less
Award ID(s):
1927554
NSF-PAR ID:
10294389
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Page Range / eLocation ID:
5084 to 5116
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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