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

This paper investigates how Natural Language Understanding (NLU) could be
applied in Emotion Recognition, a specific task in affective computing. We
finetuned different transformers language models (BERT, DistilBERT, RoBERTa,
XLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in
terms of performance (f1-score) and time to complete.

本文研究了如何在情感计算中应用自然语言理解技术，通过对不同 transformers 语言模型在细粒度情感数据集上的微调并对其性能（F1 度量）和完成时间进行评估。