# Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: 'UFAL Submission to the SIGTYP 2020 Shared Task

arXiv: https://arxiv.org/abs/2010.03920 · HTML: https://mlsanity.com/paper/2010.03920/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [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)
- [The Past, Present, and Future of Typological Databases in NLP](https://mlsanity.com/paper/2310.13440/) (2023)
- [data2lang2vec: Data Driven Typological Features Completion](https://mlsanity.com/paper/2409.17373/) (2024)
- [Typological Feature Prediction with Large Language Models: An In-Context Learning Approach](https://mlsanity.com/paper/2609.03775/) (2026)
- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7% on the test data and rank first in the shared task.