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        <title>OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models</title>
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    <abstract>Since the outbreak of coronavirus at the end of 2019, there have been numerous studies on coro- navirus in the NLP arena. Meanwhile, Twitter has been a valuable source of news and a pub- lic medium for the conveyance of information and personal expression. This paper describes the system developed by the Ochadai team for the Social Media Mining for Health Appli- cations (SMM4H) 2021 Task 5, which aims to automatically distinguish English tweets that self-report potential cases of COVID-19 from those that do not. We proposed a model ensemble that leverages pre-trained represen- tations from COVID-Twitter-BERT (Müller et al., 2020), RoBERTa (Liu et al., 2019), and Twitter-RoBERTa (Glazkova et al., 2021). Our model obtained F1-scores of 76% on the test set in the evaluation phase, and 77.5% in the post-evaluation phase.</abstract>
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    <identifier type="doi">10.18653/v1/2021.smm4h-1.25</identifier>
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        <date>2021-06</date>
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%0 Conference Proceedings
%T OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models
%A Luo, Ying
%A Pereira, Lis
%A Ichiro, Kobayashi
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F luo-etal-2021-ochadai
%X Since the outbreak of coronavirus at the end of 2019, there have been numerous studies on coro- navirus in the NLP arena. Meanwhile, Twitter has been a valuable source of news and a pub- lic medium for the conveyance of information and personal expression. This paper describes the system developed by the Ochadai team for the Social Media Mining for Health Appli- cations (SMM4H) 2021 Task 5, which aims to automatically distinguish English tweets that self-report potential cases of COVID-19 from those that do not. We proposed a model ensemble that leverages pre-trained represen- tations from COVID-Twitter-BERT (Müller et al., 2020), RoBERTa (Liu et al., 2019), and Twitter-RoBERTa (Glazkova et al., 2021). Our model obtained F1-scores of 76% on the test set in the evaluation phase, and 77.5% in the post-evaluation phase.
%R 10.18653/v1/2021.smm4h-1.25
%U https://aclanthology.org/2021.smm4h-1.25
%U https://doi.org/10.18653/v1/2021.smm4h-1.25
%P 123-125

Markdown (Informal)

[OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models](https://aclanthology.org/2021.smm4h-1.25) (Luo et al., SMM4H 2021)

• OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models (Luo et al., SMM4H 2021)

ACL

• Ying Luo, Lis Pereira, and Kobayashi Ichiro. 2021. OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 123–125, Mexico City, Mexico. Association for Computational Linguistics.