# SIGTYP 2020 Shared Task: Prediction of Typological Features

arXiv: https://arxiv.org/abs/2010.08246 · HTML: https://mlsanity.com/paper/2010.08246/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [A Principled Framework for Evaluating on Typologically Diverse Languages](https://mlsanity.com/paper/2407.05022/) (2024)
- [Does Typological Blinding Impede Cross-Lingual Sharing?](https://mlsanity.com/paper/2101.11888/) (2021)
- [Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship](https://mlsanity.com/paper/2204.05049/) (2022)
- [Language Embeddings Sometimes Contain Typological Generalizations](https://mlsanity.com/paper/2301.08115/) (2023)
- [CreoleVal: Multilingual Multitask Benchmarks for Creoles](https://mlsanity.com/paper/2310.19567/) (2023)
- [Multilingual Gradient Word-Order Typology from Universal Dependencies](https://mlsanity.com/paper/2402.01513/) (2024)
- [Language Embeddings for Typology and Cross-lingual Transfer Learning](https://mlsanity.com/paper/2106.02082/) (2021)
- [The Past, Present, and Future of Typological Databases in NLP](https://mlsanity.com/paper/2310.13440/) (2023)
- [Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness](https://mlsanity.com/paper/2306.02646/) (2023)
- [Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+](https://mlsanity.com/paper/2510.27183/) (2025)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (2026)
- [Patterns of Persistence and Diffusibility across the World's Languages](https://mlsanity.com/paper/2401.01698/) (2024)
- [Typological Feature Prediction with Large Language Models: An In-Context Learning Approach](https://mlsanity.com/paper/2609.03775/) (2026)

## Abstract (arXiv)

Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations. However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.