# The Past, Present, and Future of Typological Databases in NLP

arXiv: https://arxiv.org/abs/2310.13440 · HTML: https://mlsanity.com/paper/2310.13440/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://mlsanity.com/paper/1807.00914/) (2018)
- [Learning Language Representations for Typology Prediction](https://mlsanity.com/paper/1707.09569/) (2017)
- [What do Language Representations Really Represent?](https://mlsanity.com/paper/1901.02646/) (2019)
- [SIGTYP 2020 Shared Task: Prediction of Typological Features](https://mlsanity.com/paper/2010.08246/) (2020)
- [Neural Factor Graph Models for Cross-lingual Morphological Tagging](https://mlsanity.com/paper/1805.04570/) (2018)
- [A Probabilistic Generative Model of Linguistic Typology](https://mlsanity.com/paper/1903.10950/) (2019)
- [Tracking Typological Traits of Uralic Languages in Distributed Language Representations](https://mlsanity.com/paper/1711.05468/) (2017)
- [Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: 'UFAL Submission to the SIGTYP 2020 Shared Task](https://mlsanity.com/paper/2010.03920/) (2020)
- [Uncovering Probabilistic Implications in Typological Knowledge Bases](https://mlsanity.com/paper/1906.07389/) (2019)
- [Reconstructing Native Language Typology from Foreign Language Usage](https://mlsanity.com/paper/1404.6312/) (2014)
- [NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task](https://mlsanity.com/paper/2010.05985/) (2020)

## Built on by (most cited first, 7 of 7)

- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (2025)
- [Multilingual Gradient Word-Order Typology from Universal Dependencies](https://mlsanity.com/paper/2402.01513/) (2024)
- [From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive Grammars](https://mlsanity.com/paper/2411.15577/) (2024)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (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)

## Abstract (arXiv)

Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. Unfortunately, current large-scale typological databases, notably WALS and Grambank, are inconsistent both with each other and with other sources of typological information, such as linguistic grammars. Some of these inconsistencies stem from coding errors or linguistic variation, but many of the disagreements are due to the discrete categorical nature of these databases. We shed light on this issue by systematically exploring disagreements across typological databases and resources, and their uses in NLP, covering the past and present. We next investigate the future of such work, offering an argument that a continuous view of typological features is clearly beneficial, echoing recommendations from linguistics. We propose that such a view of typology has significant potential in the future, including in language modeling in low-resource scenarios.