论文标题
Wnut-2020任务2：深度学习模型Roberta，用于识别信息丰富的COVID-19英语推文
NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
论文作者
论文摘要
本文介绍了NIT_COVID-19团队提交的模型，该团队在Wnut-2020 Task2上为识别的COVID-19英语推文提供了信息。此共享任务解决了自动识别英语推文是否与信息丰富（新颖的冠状病毒）相关的问题。这些内容丰富的推文提供有关被恢复，确认，疑似和死亡案件以及案件的位置或旅行历史的信息。拟议的方法包括预处理技术和预先培训的罗伯塔术，并配备适合英语冠状病毒推文分类的超参数。在F1得分度量指标中，建议的模型为共享任务Wnut 2020 Task2实现的性能为89.14％。
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.
