# Unsupervised Multilingual Alignment using Wasserstein Barycenter

arXiv: https://arxiv.org/abs/2002.00743 · HTML: https://mlsanity.com/paper/2002.00743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Embedding Empirical Distributions for Computing Optimal Transport Maps](https://mlsanity.com/paper/2504.17740/) (2025)
- [Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments](https://mlsanity.com/paper/2608.22024/) (2026)

## Abstract (arXiv)

We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data. One popular strategy is to reduce multilingual alignment to the much simplified bilingual setting, by picking one of the input languages as the pivot language that we transit through. However, it is well-known that transiting through a poorly chosen pivot language (such as English) may severely degrade the translation quality, since the assumed transitive relations among all pairs of languages may not be enforced in the training process. Instead of going through a rather arbitrarily chosen pivot language, we propose to use the Wasserstein barycenter as a more informative "mean" language: it encapsulates information from all languages and minimizes all pairwise transportation costs. We evaluate our method on standard benchmarks and demonstrate state-of-the-art performances.