# Towards Language Agnostic Universal Representations

arXiv: https://arxiv.org/abs/1809.08510 · HTML: https://mlsanity.com/paper/1809.08510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning](https://mlsanity.com/paper/2505.13628/) (2025)

## Abstract (arXiv)

When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in,even if the math lessons were only taught in one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion. We learn these representations by taking inspiration from linguistics and formalizing Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970). We demonstrate the capabilities of these representations by showing that the models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.