Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they're observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (disaster relief organizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of data. In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. Our most successful model is an ensemble of transformers including RoBERTa, XLNet, and BERTweet trained in a semi-supervised experimental setting. The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard), and shows significant gains in performance compared to a baseline system using fasttext embeddings.

中文翻译：

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EdinburghNLP 在 WNUT-2020 任务 2：利用具有广义增强的 Transformer 识别 COVID-19 推文中的信息量

Twitter 已成为紧急情况下的重要沟通渠道。智能手机无处不在，人们可以实时宣布他们正在观察的紧急情况。正因为如此，越来越多的机构对以编程方式监控 Twitter（救灾组织和新闻机构）感兴趣，因此识别推文的信息量有助于过滤大量数据中的噪音。在本文中，我们展示了我们提交的 WNUT-2020 任务 2：识别信息丰富的 COVID-19 英文推文。我们最成功的模型是一组 Transformer，包括在半监督实验环境中训练的 RoBERTa、XLNet 和 BERTweet。所提出的系统在测试集上的 F1 分数为 0.9011（在排行榜上排名第 7），