The goal of this study is to investigate whether a Transformer-based neural
language model infers lexical semantics and use this information for the
completion of morphosyntactic patterns. The semantic properties considered are
telicity (also combined with definiteness) and agentivity. Both act at the
interface between semantics and morphosyntax: they are semantically determined
and syntactically encoded. The tasks were submitted to both the computational
model and a group of Italian native speakers. The comparison between the two
groups of data allows us to investigate to what extent neural language models
capture significant aspects of human semantic competence.

研究目的是调查基于 Transformer 的神经语言模型是否能推断词汇语义，并利用这些信息来完成形态句法模式的构建；研究考虑了 telicity（兼具确定性）和 agentivity 两个语义特性，它们在语义和句法语言之间的交接处起作用，既受语义决定，又在句法上编码；实验分别让计算模型和一群意大利母语者完成了这些任务，通过比较两组数据，我们可以研究神经语言模型在多大程度上捕捉到人类语义能力的重要方面。

GilBERTo 中的能动性和目的性：认知启示

Agentività e telicità in GilBERTo: implicazioni cognitive

The interpretation of the lexical aspect of verbs in English plays a crucial
role for recognizing textual entailment and learning discourse-level
inferences. We show that two elementary dimensions of aspectual class, states
vs. events, and telic vs. atelic events, can be modelled effectively with
distributional semantics. We find that a verb's local context is most
indicative of its aspectual class, and demonstrate that closed class words tend
to be stronger discriminating contexts than content words. Our approach
outperforms previous work on three datasets. Lastly, we contribute a dataset of
human--human conversations annotated with lexical aspect and present
experiments that show the correlation of telicity with genre and discourse
goals.

研究动词词汇体现出的语态对于文本蕴涵和学习语篇层推断至关重要。我们使用分布语义学有效地模拟了动态类别的两个基本维度，状态与事件以及目的性与非目的性事件。我们发现动词的本地上下文最能表明其语态，并证明封闭类词往往比内容词更具区分能力。我们的方法优于先前的三个数据集。最后，我们提供了一个由人 - 人对话注释的词汇视角数据集，并呈现实验结果，表明终止性与文本体裁和话语目标的相关性。