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        <title>BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets</title>
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    <name type="personal">
        <namePart type="given">Yanzhu</namePart>
        <namePart type="family">Guo</namePart>
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    <name type="personal">
        <namePart type="given">Virgile</namePart>
        <namePart type="family">Rennard</namePart>
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        <namePart type="given">Christos</namePart>
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    <name type="personal">
        <namePart type="given">Michalis</namePart>
        <namePart type="family">Vazirgiannis</namePart>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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    <abstract>We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialised using a general-domain French language model CamemBERT which follows the base architecture of BERT. Experiments show that BERTweetFR outperforms all previous general-domain French language models on two downstream Twitter NLP tasks of offensiveness identification and named entity recognition. The dataset used in the offensiveness detection task is first created and annotated by our team, filling in the gap of such analytic datasets in French. We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.49</identifier>
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        <url>https://aclanthology.org/2021.wnut-1.49</url>
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        <date>2021-11</date>
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            <start>445</start>
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%0 Conference Proceedings
%T BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets
%A Guo, Yanzhu
%A Rennard, Virgile
%A Xypolopoulos, Christos
%A Vazirgiannis, Michalis
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F guo-etal-2021-bertweetfr
%X We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialised using a general-domain French language model CamemBERT which follows the base architecture of BERT. Experiments show that BERTweetFR outperforms all previous general-domain French language models on two downstream Twitter NLP tasks of offensiveness identification and named entity recognition. The dataset used in the offensiveness detection task is first created and annotated by our team, filling in the gap of such analytic datasets in French. We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.
%R 10.18653/v1/2021.wnut-1.49
%U https://aclanthology.org/2021.wnut-1.49
%U https://doi.org/10.18653/v1/2021.wnut-1.49
%P 445-450

Markdown (Informal)

[BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets](https://aclanthology.org/2021.wnut-1.49) (Guo et al., WNUT 2021)

• BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets (Guo et al., WNUT 2021)

ACL

• Yanzhu Guo, Virgile Rennard, Christos Xypolopoulos, and Michalis Vazirgiannis. 2021. BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 445–450, Online. Association for Computational Linguistics.