@inproceedings{bernier-colborne-etal-2022-transfer,
title = "Transfer Learning Improves {F}rench Cross-Domain Dialect Identification: {NRC} @ {V}ar{D}ial 2022",
author = "Bernier-Colborne, Gabriel and
Leger, Serge and
Goutte, Cyril",
editor = {Scherrer, Yves and
Jauhiainen, Tommi and
Ljube{\v{s}}i{\'c}, Nikola and
Nakov, Preslav and
Tiedemann, J{\"o}rg and
Zampieri, Marcos},
booktitle = "Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.vardial-1.12",
pages = "109--118",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022
%A Bernier-Colborne, Gabriel
%A Leger, Serge
%A Goutte, Cyril
%Y Scherrer, Yves
%Y Jauhiainen, Tommi
%Y Ljubešić, Nikola
%Y Nakov, Preslav
%Y Tiedemann, Jörg
%Y Zampieri, Marcos
%S Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F bernier-colborne-etal-2022-transfer
%X 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.
%U https://aclanthology.org/2022.vardial-1.12
%P 109-118
Markdown (Informal)
[Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022](https://aclanthology.org/2022.vardial-1.12) (Bernier-Colborne et al., VarDial 2022)
ACL