Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022

Gabriel Bernier-Colborne, Serge Leger, Cyril Goutte

Abstract
We describe the systems developed by the National Research Council Canada for the French Cross-Domain Dialect Identification shared task at the 2022 VarDial evaluation campaign. We evaluated two different approaches to this task: SVM and probabilistic classifiers exploiting n-grams as features, and trained from scratch on the data provided; and a pre-trained French language model, CamemBERT, that we fine-tuned on the dialect identification task. The latter method turned out to improve the macro-F1 score on the test set from 0.344 to 0.430 (25% increase), which indicates that transfer learning can be helpful for dialect identification.
Anthology ID:
2022.vardial-1.12
Volume:
Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Yves Scherrer, Tommi Jauhiainen, Nikola Ljubešić, Preslav Nakov, Jörg Tiedemann, Marcos Zampieri
Venue:
VarDial
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
109–118
Language:
URL:
https://aclanthology.org/2022.vardial-1.12
DOI:
Bibkey:
Cite (ACL):
Gabriel Bernier-Colborne, Serge Leger, and Cyril Goutte. 2022. Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022. In Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects, pages 109–118, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022 (Bernier-Colborne et al., VarDial 2022)
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