Prior research has explored the ability of computational models to predict a
word semantic fit with a given predicate. While much work has been devoted to
modeling the typicality relation between verbs and arguments in isolation, in
this paper we take a broader perspective by assessing whether and to what
extent computational approaches have access to the information about the
typicality of entire events and situations described in language (Generalized
Event Knowledge). Given the recent success of Transformers Language Models
(TLMs), we decided to test them on a benchmark for the \textit{dynamic
estimation of thematic fit}. The evaluation of these models was performed in
comparison with SDM, a framework specifically designed to integrate events in
sentence meaning representations, and we conducted a detailed error analysis to
investigate which factors affect their behavior. Our results show that TLMs can
reach performances that are comparable to those achieved by SDM. However,
additional analysis consistently suggests that TLMs do not capture important
aspects of event knowledge, and their predictions often depend on surface
linguistic features, such as frequent words, collocations and syntactic
patterns, thereby showing sub-optimal generalization abilities.

本文研究了计算模型预测给定谓词的语义匹配能力。作者考虑了问提中的更广泛视角，评估了计算方法在评估语言描述的事件和情况（广义事件知识）的典型性信息方面的应用。结果表明 TLM 与 SDM 相当，但 TLM 的预测通常取决于表面语言特征，因此显示出次优的推广能力。

猫喝咖啡了吗？挑战以普适事件知识为基础的转换器

Did the Cat Drink the Coffee? Challenging Transformers with Generalized  Event Knowledge

Inspired by the double temporality characteristic of narrative texts, we
propose a novel approach for acquiring rich temporal "before/after" event
knowledge across sentences in narrative stories. The double temporality states
that a narrative story often describes a sequence of events following the
chronological order and therefore, the temporal order of events matches with
their textual order. We explored narratology principles and built a weakly
supervised approach that identifies 287k narrative paragraphs from three large
text corpora. We then extracted rich temporal event knowledge from these
narrative paragraphs. Such event knowledge is shown useful to improve temporal
relation classification and outperform several recent neural network models on
the narrative cloze task.

本文基于叙述文本中的双重时间特征，提出了一种新颖的方法来获取跨句子的丰富的事件先后顺序知识。通过探索叙述学原则和构建弱监督学习，从三个大型文本语料库中识别了 287k 个叙述段落，并从中提取了丰富的事件时间知识，这种事件知识对于改善时间关系分类任务很有用，并在叙述 Cloze 任务上优于最近几个神经网络模型。