论文标题
Wnut-2020任务2：使用结合和对抗性训练来识别信息丰富的COVID-19的推文
NutCracker at WNUT-2020 Task 2: Robustly Identifying Informative COVID-19 Tweets using Ensembling and Adversarial Training
论文作者
论文摘要
我们尝试使用Covid-Twitter-Bert和Roberta模型来确定信息丰富的Covid-19推文。我们进一步尝试对抗训练，以使我们的模型强大。在Wnut-2020任务2的测试数据上，Covid-Twitter-Bert和Roberta的合奏获得了0.9096（在正类上）的F1得分，并在排行榜上排名第一。使用对抗训练训练的模型的合奏也会产生相似的结果。
We experiment with COVID-Twitter-BERT and RoBERTa models to identify informative COVID-19 tweets. We further experiment with adversarial training to make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task 2 and ranks 1st on the leaderboard. The ensemble of the models trained using adversarial training also produces similar result.
