LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets

Abhilasha Sancheti, Kushal Chawla, Gaurav Verma


Abstract
In this work, we describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.
Anthology ID:
2020.wnut-1.65
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
444–449
Language:
URL:
https://aclanthology.org/2020.wnut-1.65
DOI:
10.18653/v1/2020.wnut-1.65
Bibkey:
Cite (ACL):
Abhilasha Sancheti, Kushal Chawla, and Gaurav Verma. 2020. LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 444–449, Online. Association for Computational Linguistics.
Cite (Informal):
LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets (Sancheti et al., WNUT 2020)
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PDF:
https://aclanthology.org/2020.wnut-1.65.pdf