@inproceedings{fleming-etal-2021-fine,
title = "Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of {COVID}-19 in Tweets",
author = "Fleming, Max and
Dondeti, Priyanka and
Dreisbach, Caitlin and
Poliak, Adam",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.28",
doi = "10.18653/v1/2021.smm4h-1.28",
pages = "131--134",
abstract = "We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).",
}
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<abstract>We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).</abstract>
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%0 Conference Proceedings
%T Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets
%A Fleming, Max
%A Dondeti, Priyanka
%A Dreisbach, Caitlin
%A Poliak, Adam
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F fleming-etal-2021-fine
%X We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).
%R 10.18653/v1/2021.smm4h-1.28
%U https://aclanthology.org/2021.smm4h-1.28
%U https://doi.org/10.18653/v1/2021.smm4h-1.28
%P 131-134
Markdown (Informal)
[Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets](https://aclanthology.org/2021.smm4h-1.28) (Fleming et al., SMM4H 2021)
ACL