mlsanityData to 7 Oct 2026

A multilabel approach to morphosyntactic probing

arXiv 2104.08464 ↗First posted 17 Apr 2021cs.CLcited by 8

Not on the map. Why?

Abstract (arXiv)

We introduce a multilabel probing task to assess the morphosyntactic representations of word embeddings from multilingual language models. We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages of varying morphological complexity: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we show that multilingual BERT renders many morphosyntactic features easily and simultaneously extractable (e.g., gender, grammatical case, pronominal type). We further evaluate the probes on six "held-out" languages in a zero-shot transfer setting: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This style of probing has the added benefit of revealing the linguistic properties that language models recognize as being shared across languages. For instance, the probes performed well on recognizing nouns in the held-out languages, suggesting that multilingual BERT has a conception of noun-hood that transcends individual languages; yet, the same was not true of adjectives.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 30 of its 60 references that are in the map. First posted on arXiv 17 Apr 2021.

Builds on

30 of 60 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  5. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  7. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  8. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  9. 2019How multilingual is Multilingual BERT?cited by 642
  10. 2019Designing and Interpreting Probes with Control Taskscited by 608
  11. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 452
  12. 2019Linguistic Knowledge and Transferability of Contextual Representationscited by 382
18 more
  1. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
  2. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 269
  3. 2018Targeted Syntactic Evaluation of Language Modelscited by 239
  4. 2018Colorless green recurrent networks dream hierarchicallycited by 210
  5. 2020Information-Theoretic Probing for Linguistic Structurecited by 204
  6. 2019Cross-Lingual Ability of Multilingual BERT: An Empirical Studycited by 182
  7. 2019Emerging Cross-lingual Structure in Pretrained Language Modelscited by 180
  8. 2017What do Neural Machine Translation Models Learn about Morphology?cited by 165
  9. 2020Finding Universal Grammatical Relations in Multilingual BERTcited by 108
  10. 2018Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Taskscited by 56
  11. 2020Pareto Probing: Trading Off Accuracy for Complexitycited by 55
  12. 2019The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflectioncited by 43
  13. 2020Morphology Matters: A Multilingual Language Modeling Analysiscited by 37
  14. 2019LINSPECTOR: Multilingual Probing Tasks for Word Representationscited by 22
  15. 2020A Systematic Analysis of Morphological Content in BERT Models for Multiple Languagescited by 20
  16. 2018Do RNNs learn human-like abstract word order preferences?cited by 10
  17. 2019Does BERT agree? Evaluating knowledge of structure dependence through agreement relationscited by 9
  18. 2019Better Character Language Modeling Through Morphologycited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

8 papers.