BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models

TL;DR

This paper presents an ensemble deep learning and transfer learning approach to accurately identify informative COVID-19 tweets, achieving high F1 scores on a labeled dataset.

Contribution

The study introduces a novel ensemble of transformer and deep learning models for COVID-19 tweet classification, improving detection accuracy.

Findings

Achieved 88.81% F1 score on test set

Ensemble model outperforms individual models

Effective combination of transfer learning techniques

Abstract

The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.