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        <title>LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models</title>
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        <namePart type="given">Dasun</namePart>
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        <namePart type="given">Menasha</namePart>
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            <namePart type="given">Dongfang</namePart>
            <namePart type="family">Xu</namePart>
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    <abstract>This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.</abstract>
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%0 Conference Proceedings
%T LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models
%A Athukoralage, Dasun
%A Atapattu, Thushari
%A Thilakaratne, Menasha
%A Falkner, Katrina
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F athukoralage-etal-2024-lt4sg
%X This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.
%U https://aclanthology.org/2024.smm4h-1.9
%P 38-41

Markdown (Informal)

[LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models](https://aclanthology.org/2024.smm4h-1.9) (Athukoralage et al., SMM4H-WS 2024)

• LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models (Athukoralage et al., SMM4H-WS 2024)

ACL

• Dasun Athukoralage, Thushari Atapattu, Menasha Thilakaratne, and Katrina Falkner. 2024. LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 38–41, Bangkok, Thailand. Association for Computational Linguistics.