Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system EVELINK to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitation of the current model. Overall, as our analysis shows, Event Linking is a considerably challenging and essential task requiring more effort from the community. Data and code are available here: https://github.com/CogComp/event-linking.

翻译：理解一篇文章需要掌握其构成事件。然而，提及事件的上下文往往缺乏该事件的细节。问题随之产生：除了文章局部语境提供的信息外，读者如何获取关于特定事件的更多知识？本文定义了事件链接（Event Linking）这一新的事件级自然语言理解任务。该任务旨在将文章中提及的事件与最相关的维基百科页面建立关联，期望该页面能提供关于事件所指代内容的丰富知识。为规范这一新研究方向，我们做出了四方面贡献。首先，本研究是学界首次正式定义事件链接任务。其次，我们为该任务构建了数据集：具体而言，我们从维基百科自动收集训练集，并创建两个评估集——其一来自维基百科领域（报告领域内性能），其二来自真实新闻领域（评估跨领域性能）。第三，我们重新训练并评估了两个最先进的实体链接模型，揭示了事件链接的挑战性，并提出了事件专用链接系统EVELINK，为这一新任务设立了具有竞争力的基准。第四，我们开展了详尽深入的分析，以帮助理解任务本质及当前模型的局限性。总体而言，正如分析所示，事件链接是一项极具挑战性的基础任务，亟需学界投入更多努力。数据和代码现已开放：https://github.com/CogComp/event-linking。