In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter. The objective of this task is to extract answers from COVID-related tweets to a set of predefined slot-filling questions. Our approach treats the event extraction task as a question answering task by leveraging the transformer-based T5 text-to-text model. According to the official evaluation scores returned, namely F1, our submitted run achieves competitive performance compared to other participating runs (Top 3). However, we argue that this evaluation may underestimate the actual performance of runs based on text-generation. Although some such runs may answer the slot questions well, they may not be an exact string match for the gold standard answers. To measure the extent of this underestimation, we adopt a simple exact-answer transformation method aiming at converting the well-answered predictions to exactly-matched predictions. The results show that after this transformation our run overall reaches the same level of performance as the best participating run and state-of-the-art F1 scores in three of five COVID-related events. Our code is publicly available to aid reproducibility

中文翻译：

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W-NUT 2020 上的 UCD-CS 共享任务 3：社交媒体上 COVID-19 事件提取的文本到文本方法

在本文中，我们描述了我们在共享任务中的方法：从 Twitter 中提取 COVID-19 事件。此任务的目标是从与 COVID 相关的推文中提取一组预定义的填空问题的答案。我们的方法通过利用基于转换器的 T5 文本到文本模型将事件提取任务视为问答任务。根据返回的官方评估分数，即 F1，我们提交的运行与其他参与运行（前 3 名）相比具有竞争力。然而，我们认为这种评估可能低估了基于文本生成的运行的实际性能。尽管某些此类运行可能会很好地回答插槽问题，但它们可能与黄金标准答案的字符串不完全匹配。为了衡量这种低估的程度，我们采用了一种简单的精确答案转换方法，旨在将回答良好的预测转换为精确匹配的预测。结果表明，在这种转变之后，我们的整体运行达到了与最佳参与运行相同的性能水平，并且在五个 COVID 相关事件中的三个中达到了最先进的 F1 分数。我们的代码是公开可用的，以帮助重现性