@inproceedings{palmer-etal-2022-chaai,
title = "{CHAAI}@{SMM}4{H}{'}22: {R}o{BERT}a, {GPT}-2 and Sampling - An interesting concoction",
author = "Palmer, Christopher and
Khademi Habibabadi, Sedigheh and
Javed, Muhammad and
Dimaguila, Gerardo Luis and
Buttery, Jim",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy",
booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.smm4h-1.24",
pages = "81--84",
abstract = "This paper describes the approaches to the SMM4H 2022 Shared Tasks that were taken by our team for tasks 1 and 6. Task 6 was the {``}Classification of tweets which indicate self-reported COVID-19 vaccination status (in English){''}. The best test F1 score was 0.82 using a CT-BERT model, which exceeded the median test F1 score of 0.77, and was close to the 0.83 F1 score of the SMM4H baseline model. Task 1 was described as the {``}Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English){''}. We undertook task 1a, and with a RoBERTa-base model achieved an F1 Score of 0.61 on test data, which exceeded the mean test F1 for the task of 0.56.",
}
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<abstract>This paper describes the approaches to the SMM4H 2022 Shared Tasks that were taken by our team for tasks 1 and 6. Task 6 was the “Classification of tweets which indicate self-reported COVID-19 vaccination status (in English)”. The best test F1 score was 0.82 using a CT-BERT model, which exceeded the median test F1 score of 0.77, and was close to the 0.83 F1 score of the SMM4H baseline model. Task 1 was described as the “Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English)”. We undertook task 1a, and with a RoBERTa-base model achieved an F1 Score of 0.61 on test data, which exceeded the mean test F1 for the task of 0.56.</abstract>
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%0 Conference Proceedings
%T CHAAI@SMM4H’22: RoBERTa, GPT-2 and Sampling - An interesting concoction
%A Palmer, Christopher
%A Khademi Habibabadi, Sedigheh
%A Javed, Muhammad
%A Dimaguila, Gerardo Luis
%A Buttery, Jim
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F palmer-etal-2022-chaai
%X This paper describes the approaches to the SMM4H 2022 Shared Tasks that were taken by our team for tasks 1 and 6. Task 6 was the “Classification of tweets which indicate self-reported COVID-19 vaccination status (in English)”. The best test F1 score was 0.82 using a CT-BERT model, which exceeded the median test F1 score of 0.77, and was close to the 0.83 F1 score of the SMM4H baseline model. Task 1 was described as the “Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English)”. We undertook task 1a, and with a RoBERTa-base model achieved an F1 Score of 0.61 on test data, which exceeded the mean test F1 for the task of 0.56.
%U https://aclanthology.org/2022.smm4h-1.24
%P 81-84
Markdown (Informal)
[CHAAI@SMM4H’22: RoBERTa, GPT-2 and Sampling - An interesting concoction](https://aclanthology.org/2022.smm4h-1.24) (Palmer et al., SMM4H 2022)
ACL
- Christopher Palmer, Sedigheh Khademi Habibabadi, Muhammad Javed, Gerardo Luis Dimaguila, and Jim Buttery. 2022. CHAAI@SMM4H’22: RoBERTa, GPT-2 and Sampling - An interesting concoction. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 81–84, Gyeongju, Republic of Korea. Association for Computational Linguistics.