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Millions of people around the world are sharing COVID-19 related informationon social media platforms. Since not all the information shared on the socialmedia is useful, a machine learning system to identify informative posts canhelp users in finding relevant information. In this paper, we present a BERTclassifier system for W-NUT2020 Shared Task 2: Identification of InformativeCOVID-19 English Tweets. Further, we show that BERT exploits some easy signalsto identify informative tweets, and adding simple patterns to uninformativetweets drastically degrades BERT performance. In particular, simply adding 10deaths to tweets in dev set, reduces BERT F1- score from 92.63 to 7.28. We alsopropose a simple data augmentation technique that helps in improving therobustness and generalization ability of the BERT classifier.

NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative