@inproceedings{dhana-laxmi-etal-2020-dsc,
title = "{DSC}-{IIT} {ISM} at {WNUT}-2020 Task 2: Detection of {COVID}-19 informative tweets using {R}o{BERT}a",
author = "Dhana Laxmi, Sirigireddy and
Agarwal, Rohit and
Sinha, Aman",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.58",
doi = "10.18653/v1/2020.wnut-1.58",
pages = "409--413",
abstract = "Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.",
}
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<abstract>Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.</abstract>
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%0 Conference Proceedings
%T DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa
%A Dhana Laxmi, Sirigireddy
%A Agarwal, Rohit
%A Sinha, Aman
%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 dhana-laxmi-etal-2020-dsc
%X Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.
%R 10.18653/v1/2020.wnut-1.58
%U https://aclanthology.org/2020.wnut-1.58
%U https://doi.org/10.18653/v1/2020.wnut-1.58
%P 409-413
Markdown (Informal)
[DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa](https://aclanthology.org/2020.wnut-1.58) (Dhana Laxmi et al., WNUT 2020)
ACL