The capabilities and limitations of BERT and similar models are still unclear when it comes to learning syntactic abstractions, in particular across languages. In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties. We show that this task is deceptively simple, with easy gains offset by a long tail of harder cases, and that BERT's zero-shot performance is dominated by word-order effects, mirroring the SVO/VSO/SOV typology.
@article{arxiv.2205.11987,
title = {Word-order typology in Multilingual BERT: A case study in subordinate-clause detection},
author = {Dmitry Nikolaev and Sebastian Padó},
journal= {arXiv preprint arXiv:2205.11987},
year = {2022}
}
Comments
Accepted for publication in the proceedings of SIGTYP workshop 2022