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        <title>mattica@SMM4H’22: Leveraging sentiment for stance &amp; premise joint learning</title>
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            <title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop &amp; Shared Task</title>
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            <namePart type="given">Graciela</namePart>
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    <abstract>This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which we fine-tuned by combining different strategies such as leveraging an additional stance detection dataset through two-stage fine-tuning, joint-learning Stance and Premise detection objectives; and ensembling the sentiment-polarity given by an off-the-shelf fine-tuned model.</abstract>
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%0 Conference Proceedings
%T mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning
%A Lithgow-Serrano, Oscar
%A Cornelius, Joseph
%A Rinaldi, Fabio
%A Dolamic, Ljiljana
%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 lithgow-serrano-etal-2022-mattica
%X This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which we fine-tuned by combining different strategies such as leveraging an additional stance detection dataset through two-stage fine-tuning, joint-learning Stance and Premise detection objectives; and ensembling the sentiment-polarity given by an off-the-shelf fine-tuned model.
%U https://aclanthology.org/2022.smm4h-1.22
%P 75-77

Markdown (Informal)

[mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning](https://aclanthology.org/2022.smm4h-1.22) (Lithgow-Serrano et al., SMM4H 2022)

• mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning (Lithgow-Serrano et al., SMM4H 2022)

ACL

• Oscar Lithgow-Serrano, Joseph Cornelius, Fabio Rinaldi, and Ljiljana Dolamic. 2022. mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 75–77, Gyeongju, Republic of Korea. Association for Computational Linguistics.