mlsanityData to 7 Oct 2026

Multilingual Gradient Word-Order Typology from Universal Dependencies

arXiv 2402.01513 ↗First posted 2 Feb 2024cs.CLcited by 9

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Abstract (arXiv)

While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological databases, including WALS and Grambank, suffer from inconsistencies primarily caused by their categorical format. Furthermore, typological categorisations by definition differ significantly from the continuous nature of phenomena, as found in natural language corpora. In this paper, we introduce a new seed dataset made up of continuous-valued data, rather than categorical data, that can better reflect the variability of language. While this initial dataset focuses on word-order typology, we also present the methodology used to create the dataset, which can be easily adapted to generate data for a broader set of features and languages.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

12 of 30 references.

References · 12

  1. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 269
  2. 2018Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processingcited by 63
  3. 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
  4. 2017Learning Language Representations for Typology Predictioncited by 40
  5. 2016Continuous multilinguality with language vectorscited by 20
  6. 2017Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languagescited by 13
  7. 2018From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddingscited by 13
  8. 2020SIGTYP 2020 Shared Task: Prediction of Typological Featurescited by 13
  9. 2017Tracking Typological Traits of Uralic Languages in Distributed Language Representationscited by 7
  10. 2023The Past, Present, and Future of Typological Databases in NLPcited by 7
  11. 2021SIGTYP 2021 Shared Task: Robust Spoken Language Identificationcited by 5
  12. 2022Word-order typology in Multilingual BERT: A case study in subordinate-clause detectioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 3.

9 papers.

  1. 2025MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairscited by 65
  2. 2024What is "Typological Diversity" in NLP?cited by 3
  3. 2025Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accentcited by 3
  4. 2025Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysiscited by 2
  5. 2025On the Interplay between Positional Encodings, Morphological Complexity, and Word Order Flexibilitycited by 1
  6. 2026Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Modelscited by 1
  7. 2025Extending dependencies to the taggedPBC: Word order in transitive clausescited by 0
  8. 2026Evaluating Metalinguistic Knowledge in Large Language Models across the World's Languagescited by 0
  9. 2026Vocabulary shapes cross-lingual variation of word-order learnability in language modelscited by 0