Morphosyntactic probing of multilingual BERT models

Judit Acs*, Endre Hamerlik, Roy Schwartz, Noah A. Smith, Andras Kornai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks. We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks. We then apply two methods to locate, for each probing task, where the disambiguating information resides in the input. The first is a new perturbation method that masks various parts of context; the second is the classical method of Shapley values. The most intriguing finding that emerges is a strong tendency for the preceding context to hold more information relevant to the prediction than the following context.

Original languageAmerican English
JournalNatural Language Engineering
Issue number1
StatePublished - 25 May 2023

Bibliographical note

Publisher Copyright:
© The Author(s), 2023. Published by Cambridge University Press.


  • Language Models
  • Language Resources
  • Machine Learning
  • Morphology
  • Multilinguality


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