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.

本论文提出了一种基于序列建模的方法，利用大型语料库中显式和隐含的时间常识，构建了一个新颖的时间常识语言模型 TACOLM。该模型可以有效预测各种维度的时间常识，并且在时态 NLP 相关任务中产生了比标准 BERT 更好的事件表示。