论文标题
暂时的常识获取，最少的监督
Temporal Common Sense Acquisition with Minimal Supervision
论文作者
论文摘要
时间常识（例如，事件的持续时间和频率）对于理解自然语言至关重要。但是，其收购具有挑战性，部分原因是这种信息通常在文本中没有明确表示，并且对这种概念的人类注释是昂贵的。这项工作提出了一种新颖的序列建模方法，该方法利用了从大型语料库中提取的时间常识的明确和隐式提及，以构建塔科尔姆，这是一种时间常识性语言模型。我们的方法显示出对时间常识的各个方面的质量预测（在UDST上和来自Realnews的新收集的数据集）。它还为相关任务（例如持续时间比较，亲子关系，事件核心和时间质量质量质量标准（在TimeBank，Hieve和McTaco上）等相关任务的事件表示，它们比使用标准BERT更好。因此，它将是时间NLP的重要组成部分。
Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TACOLM, a temporal common sense language model. Our method is shown to give quality predictions of various dimensions of temporal common sense (on UDST and a newly collected dataset from RealNews). It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the standard BERT. Thus, it will be an important component of temporal NLP.
