EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets

• URL: http://arxiv.org/abs/2009.06375v3
• Date: Sun, 18 Apr 2021 12:28:14 GMT
• Title: EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets
• Authors: Nickil Maveli
• Abstract summary: We present our submission for WNUT Task 2: Identification of informative COVID-19 English Tweets. Our most successful model is an ensemble of transformers, including RoBERTa, XLNet, and BERTweet trained in a Semi-Supervised Learning (SSL) setting. The proposed system achieves an F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) and shows significant gains in performance compared to a system using FastText embeddings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Twitter and, in general, social media has become an indispensable communication channel in times of emergency. The ubiquitousness of smartphone gadgets enables people to declare an emergency observed in real-time. As a result, more agencies are interested in programmatically monitoring Twitter (disaster relief organizations and news agencies). Therefore, recognizing the informativeness of a Tweet can help filter noise from the large volumes of Tweets. In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. Our most successful model is an ensemble of transformers, including RoBERTa, XLNet, and BERTweet trained in a Semi-Supervised Learning (SSL) setting. The proposed system achieves an F1 score of 0.9011 on the test set (ranking 7th on the leaderboard) and shows significant gains in performance compared to a baseline system using FastText embeddings.

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