论文标题
WNUT-2020任务2：使用Covid Twitter Bert和Bagging Ensemble Technique基于多数投票的序列分类2：序列分类
Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting
论文作者
论文摘要
本文介绍了我们针对EMNLP Wnut-2020共享任务2：识别信息丰富的COVID-19英语推文的方法。任务是开发一个系统，该系统自动识别与新型冠状病毒（COVID-19）相关的英语推文是否有用。我们在三个阶段解决了任务。第一阶段涉及通过仅过滤相关信息来预处理数据集。随后，进行了多种深度学习模型，例如CNN，RNN和基于变压器的模型。在最后阶段，我们提出了在提供的数据集的不同子集上训练的最佳模型的合奏。我们的最终方法的F1得分为0.9037，并以F1分数为评估标准排名第六。
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.
