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Neural Detection of Cross-lingual Syntactic Knowledge

In recent years, there has been prominent development in pre-trained multilingual language models, such as mBERT, XLM-R, etc., which are able to capture and learn linguistic knowledge from input across a variety of languages simultaneously. How-ever, little is known about where multilingual models localise what they have learnt across languages. In this paper, we specifically evaluate cross-lingual syntactic information embedded in CINO, a more recent multilingual pre-trained language model. We probe CINO on Universal Dependencies treebank datasets of English and Chinese Mandarin for two syntax-related layer-wise evaluation tasks: Part-of-Speech Tagging at token level and Syntax Tree-depth Prediction at sentence level. The results of our layer-wise probing experiments show that token-level syntax is localisable in higher layers and consistency is shown across the typologically different languages, whereas sentence-level syntax is distributed across the layers in typology-specific and universal manners.