论文标题
POQUE：提出特定于参与者的结果问题，以深入了解复杂事件
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
论文作者
论文摘要
关于结果的知识对于复杂的事件理解至关重要，但很难获得。我们表明，通过预先识别复杂活动的参与者，人群工人能够（1）推断构成情况的显着事件的集体影响，（2）注释参与者在导致情况下的意志参与，（3）基于参与者州变化的状况的结果。通过创建一个多步界面和仔细的质量控制策略，我们通过高质量的新闻叙事和rocstories收集了高质量的注释数据集，该数据集具有高通道间协议（0.74-0.96加权Fleiss Kappa）。我们的数据集Poque（参与者的结果问题），可以探索和开发用于解决语义理解的多个方面的模型。在实验上，我们表明当前的语言模型通过我们的任务配方以微妙的方式落后于人类绩效，这些任务制定针对复杂事件的抽象和特定理解，其结果以及参与者对事件的影响。
Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant's influence over the event culmination.
