NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets

TL;DR

This paper describes a deep learning approach using RoBERTa to automatically identify informative COVID-19 related tweets in English, achieving high accuracy in a shared task.

Contribution

The paper introduces a RoBERTa-based model with specific preprocessing and hyperparameters for classifying COVID-19 tweets as informative or not.

Findings

Achieved 89.14% F1-score on the shared task

Effective use of pre-trained RoBERTa for COVID-19 tweet classification

Improved accuracy over baseline models

Abstract

This paper presents the model submitted by the NIT_COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as the location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.