In this paper, our goal is to investigate to what degree multilingual pretrained language models capture cross-linguistically valid abstract linguistic representations. We take the approach of developing curated synthetic data on a large scale, with specific properties, and using them to study sentence representations built using pretrained language models. We use a new multiple-choice task and datasets, Blackbird Language Matrices (BLMs), to focus on a specific grammatical structural phenomenon -- subject-verb agreement across a variety of sentence structures -- in several languages. Finding a solution to this task requires a system detecting complex linguistic patterns and paradigms in text representations. Using a two-level architecture that solves the problem in two steps -- detect syntactic objects and their properties in individual sentences, and find patterns across an input sequence of sentences -- we show that despite having been trained on multilingual texts in a consistent manner, multilingual pretrained language models have language-specific differences, and syntactic structure is not shared, even across closely related languages.

本研究旨在探讨多语言预训练语言模型在捕捉跨语言的抽象语言表示方面的能力，填补了现有研究中的空白。我们提出了一种新的方法，通过大规模合成数据集和多项选择任务，研究句子中主谓一致的句法现象。研究发现，尽管模型在多语言文本上进行了一致训练，但仍存在语言特定的差异，句法结构并不共享，即使在相关语言之间亦是如此。

通过多语言主谓一致探讨句子嵌入中的句法信息