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 , 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.- Anthology ID:
- 2021.naacl-main.402
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5084–5116
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.402
- DOI:
- 10.18653/v1/2021.naacl-main.402
- Cite (ACL):
- Md Rizwan Parvez and Kai-Wei Chang. 2021. Evaluating the Values of Sources in Transfer Learning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5084–5116, Online. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating the Values of Sources in Transfer Learning (Parvez & Chang, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.naacl-main.402.pdf
- Code
- rizwan09/NLPDV
- Data
- GLUE, MultiNLI, QNLI, Universal Dependencies, XNLI