A multilabel approach to morphosyntactic probing
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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
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18 more
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Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2021Conditional probing: measuring usable information beyond a baselinecited by 61
- 2024Holmes: A Benchmark to Assess the Linguistic Competence of Language Modelscited by 26
- 2023Probing LLMs for Joint Encoding of Linguistic Categoriescited by 9
- 2023Morphosyntactic probing of multilingual BERT modelscited by 8
- 2022SocioProbe: What, When, and Where Language Models Learn about Sociodemographicscited by 7
- 2025Linguistic Interpretability of Transformer-based Language Models: a systematic reviewcited by 7
- 2023Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibrationcited by 2
- 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Modelscited by 1
- 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models9 Jan 2026
- 2025Linguistic Interpretability of Transformer-based Language Models: a systematic review9 Apr 2025
- 2024Holmes: A Benchmark to Assess the Linguistic Competence of Language Models29 Apr 2024
- 2023Probing LLMs for Joint Encoding of Linguistic Categories28 Oct 2023
- 2023Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration8 Oct 2023
- 2023Morphosyntactic probing of multilingual BERT models9 Jun 2023
- 2022SocioProbe: What, When, and Where Language Models Learn about Sociodemographics8 Nov 2022
- 2021Conditional probing: measuring usable information beyond a baseline19 Sep 2021