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        <title>Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets</title>
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    <abstract>Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks, while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-BERT with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leaderboard with F1 of 0.6598, without using any ensembles or additional datasets.</abstract>
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%0 Conference Proceedings
%T Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets
%A Kaushal, Ayush
%A Vaidhya, Tejas
%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 kaushal-vaidhya-2020-winners
%X Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks, while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-BERT with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leaderboard with F1 of 0.6598, without using any ensembles or additional datasets.
%R 10.18653/v1/2020.wnut-1.79
%U https://aclanthology.org/2020.wnut-1.79
%U https://doi.org/10.18653/v1/2020.wnut-1.79
%P 522-529

Markdown (Informal)

[Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets](https://aclanthology.org/2020.wnut-1.79) (Kaushal & Vaidhya, WNUT 2020)

• Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets (Kaushal & Vaidhya, WNUT 2020)

ACL

• Ayush Kaushal and Tejas Vaidhya. 2020. Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 522–529, Online. Association for Computational Linguistics.