@inproceedings{chanda-etal-2020-irlab,
title = "{IRL}ab@{IITBHU} at {WNUT}-2020 Task 2: Identification of informative {COVID}-19 {E}nglish Tweets using {BERT}",
author = "Chanda, Supriya and
Nandy, Eshita and
Pal, Sukomal",
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-1.56",
doi = "10.18653/v1/2020.wnut-1.56",
pages = "399--403",
abstract = "This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.",
}
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%0 Conference Proceedings
%T IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT
%A Chanda, Supriya
%A Nandy, Eshita
%A Pal, Sukomal
%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 chanda-etal-2020-irlab
%X This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.
%R 10.18653/v1/2020.wnut-1.56
%U https://aclanthology.org/2020.wnut-1.56
%U https://doi.org/10.18653/v1/2020.wnut-1.56
%P 399-403
Markdown (Informal)
[IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT](https://aclanthology.org/2020.wnut-1.56) (Chanda et al., WNUT 2020)
ACL