论文标题
Wnut-2020任务2：通过结合深度学习和转移学习模型在Twitter上识别Covid-19的信息
BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models
论文作者
论文摘要
疫情199病毒对世界各地人民的健康产生了重大影响。因此，必须与所有人一起拥有有关疾病的恒定和准确信息。本文介绍了我们针对Wnut-2020任务的预测系统2：识别信息丰富的COVID-19英语推文。此任务的数据集包含大小为10,000个由人类标记的英语标记的推文。我们的三个变压器和深度学习模型的合奏模型用于最终预测。实验结果表明，在测试集中，我们的系统的信息标签达到了F1。
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.
