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

Large neural language models are steadily contributing state-of-the-art
performance to question answering and other natural language and information
processing tasks. These models are expensive to train. We propose to evaluate
whether such pre-trained models can benefit from the addition of explicit
linguistics information without requiring retraining from scratch.
We present a linguistics-informed question answering approach that extends
and fine-tunes a pre-trained transformer-based neural language model with
symbolic knowledge encoded with a heterogeneous graph transformer. We
illustrate the approach by the addition of syntactic information in the form of
dependency and constituency graphic structures connecting tokens and virtual
vertices.
A comparative empirical performance evaluation with BERT as its baseline and
with Stanford Question Answering Dataset demonstrates the competitiveness of
the proposed approach. We argue, in conclusion and in the light of further
results of preliminary experiments, that the approach is extensible to further
linguistics information including semantics and pragmatics.

本研究探讨了如何在不需要从头开始的情况下，在预训练的神经语言模型中添加显式的语言学信息，以提高其性能，并提出了一种基于转换器的神经语言模型，通过异构图转换器对模型进行扩展和微调，这可以通过添加句法信息来实现，而基于斯坦福问答数据集的实证性能评估表明了该方法的竞争性。此方法包括语义和语用方面的信息，具有可扩展性。