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        <title>Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets</title>
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    <abstract>Social media witnessed vast amounts of misinformation being circulated every day during the Covid-19 pandemic so much so that the WHO Director-General termed the phenomenon as “infodemic.” The ill-effects of such misinformation are multifarious. Thus, identifying and eliminating the sources of misinformation becomes very crucial, especially when mass panic can be controlled only through the right information. However, manual identification is arduous, with such large amounts of data being generated every day. This shows the importance of automatic identification of misinformative posts on social media. WNUT-2020 Task 2 aims at building systems for automatic identification of informative tweets. In this paper, I discuss my approach to WNUT-2020 Task 2. I fine-tuned eleven variants of four transformer networks -BERT, RoBERTa, XLM-RoBERTa, ELECTRA, on top of two different preprocessing techniques to reap good results. My top submission achieved an F1-score of 85.3% in the final evaluation.</abstract>
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%0 Conference Proceedings
%T Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets
%A Sai, Siva
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sai-2020-siva
%X Social media witnessed vast amounts of misinformation being circulated every day during the Covid-19 pandemic so much so that the WHO Director-General termed the phenomenon as “infodemic.” The ill-effects of such misinformation are multifarious. Thus, identifying and eliminating the sources of misinformation becomes very crucial, especially when mass panic can be controlled only through the right information. However, manual identification is arduous, with such large amounts of data being generated every day. This shows the importance of automatic identification of misinformative posts on social media. WNUT-2020 Task 2 aims at building systems for automatic identification of informative tweets. In this paper, I discuss my approach to WNUT-2020 Task 2. I fine-tuned eleven variants of four transformer networks -BERT, RoBERTa, XLM-RoBERTa, ELECTRA, on top of two different preprocessing techniques to reap good results. My top submission achieved an F1-score of 85.3% in the final evaluation.
%R 10.18653/v1/2020.wnut-1.45
%U https://aclanthology.org/2020.wnut-1.45
%U https://doi.org/10.18653/v1/2020.wnut-1.45
%P 337-341

Markdown (Informal)

[Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets](https://aclanthology.org/2020.wnut-1.45) (Sai, WNUT 2020)

• Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets (Sai, WNUT 2020)

ACL

• Siva Sai. 2020. Siva at WNUT-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 337–341, Online. Association for Computational Linguistics.