This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets. The task is to develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. We solve the task in three stages. The first stage involves pre-processing the dataset by filtering only relevant information. This is followed by experimenting with multiple deep learning models like CNNs, RNNs and Transformer based models. In the last stage, we propose an ensemble of the best model trained on different subsets of the provided dataset. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria.
翻译:本文件介绍了我们用来处理EMNLP WNUT-2020共同任务2的方法:确定信息丰富的COVID-19英文Tweets。任务是开发一个系统,自动确定与新科罗纳病毒(COVID-19)有关的英语Tweet是否具有信息性。我们分三个阶段完成这项任务。第一阶段是预先处理数据集,只过滤相关信息。随后是试验多种深层次学习模型,如CNN、RNN和基于变异器的模型。在最后阶段,我们提出了关于所提供数据集不同子集的训练的最佳模型的组合。我们的最后方法达到了0.9037的F1点,我们总体上排第六位,以F1为评估标准。