Much recent effort has been devoted to creating large-scale language models.
Nowadays, the most prominent approaches are based on deep neural networks, such
as BERT. However, they lack transparency and interpretability, and are often
seen as black boxes. This affects not only their applicability in downstream
tasks but also the comparability of different architectures or even of the same
model trained using different corpora or hyperparameters. In this paper, we
propose a set of intrinsic evaluation tasks that inspect the linguistic
information encoded in models developed for Brazilian Portuguese. These tasks
are designed to evaluate how different language models generalise information
related to grammatical structures and multiword expressions (MWEs), thus
allowing for an assessment of whether the model has learned different
linguistic phenomena. The dataset that was developed for these tasks is
composed of a series of sentences with a single masked word and a cue phrase
that helps in narrowing down the context. This dataset is divided into MWEs and
grammatical structures, and the latter is subdivided into 6 tasks: impersonal
verbs, subject agreement, verb agreement, nominal agreement, passive and
connectors. The subset for MWEs was used to test BERTimbau Large, BERTimbau
Base and mBERT. For the grammatical structures, we used only BERTimbau Large,
because it yielded the best results in the MWE task.

本研究提出了一种评估方法，针对用于巴西葡萄牙文的语言模型，通过固定的语言格式，考察语言信息的编码方式，评估不同的语言模型在处理语法结构和多词表达方面的泛化能力，解决了语言模型黑盒且缺乏可解释性的问题。