# Multilingual Gradient Word-Order Typology from Universal Dependencies

arXiv: https://arxiv.org/abs/2402.01513 · HTML: https://mlsanity.com/paper/2402.01513/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://mlsanity.com/paper/1807.00914/) (2018)
- [Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review](https://mlsanity.com/paper/2305.16768/) (2023)
- [Learning Language Representations for Typology Prediction](https://mlsanity.com/paper/1707.09569/) (2017)
- [Continuous multilinguality with language vectors](https://mlsanity.com/paper/1612.07486/) (2016)
- [Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages](https://mlsanity.com/paper/1704.08914/) (2017)
- [From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings](https://mlsanity.com/paper/1802.09375/) (2018)
- [SIGTYP 2020 Shared Task: Prediction of Typological Features](https://mlsanity.com/paper/2010.08246/) (2020)
- [Tracking Typological Traits of Uralic Languages in Distributed Language Representations](https://mlsanity.com/paper/1711.05468/) (2017)
- [The Past, Present, and Future of Typological Databases in NLP](https://mlsanity.com/paper/2310.13440/) (2023)
- [SIGTYP 2021 Shared Task: Robust Spoken Language Identification](https://mlsanity.com/paper/2106.03895/) (2021)
- [Word-order typology in Multilingual BERT: A case study in subordinate-clause detection](https://mlsanity.com/paper/2205.11987/) (2022)

## Built on by (most cited first, 9 of 9)

- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent](https://mlsanity.com/paper/2505.07659/) (2025)
- [Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis](https://mlsanity.com/paper/2512.00214/) (2025)
- [On the Interplay between Positional Encodings, Morphological Complexity, and Word Order Flexibility](https://mlsanity.com/paper/2511.08139/) (2025)
- [Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models](https://mlsanity.com/paper/2602.03551/) (2026)
- [Extending dependencies to the taggedPBC: Word order in transitive clauses](https://mlsanity.com/paper/2506.06785/) (2025)
- [Evaluating Metalinguistic Knowledge in Large Language Models across the World's Languages](https://mlsanity.com/paper/2602.02182/) (2026)
- [Vocabulary shapes cross-lingual variation of word-order learnability in language models](https://mlsanity.com/paper/2603.19427/) (2026)

## 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.