BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter

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Abstract

This paper describes Kata.ai's submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three tasks: classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.

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Aji, A. F., Wibowo, H. A., Nityasya, M. N., Prasojo, R. E., & Fatyanosa, T. N. (2021). BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter. In Social Media Mining for Health, SMM4H 2021 - Proceedings of the 6th Workshop and Shared Tasks (pp. 58–64). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.smm4h-1.9

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