# A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity

arXiv: https://arxiv.org/abs/1906.01926 · HTML: https://mlsanity.com/paper/1906.01926/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2110.08151/) (2021)
- [An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces](https://mlsanity.com/paper/2109.12640/) (2021)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [Evaluating Word Embeddings with Categorical Modularity](https://mlsanity.com/paper/2106.00877/) (2021)
- [Constructing Cross-lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content](https://mlsanity.com/paper/2206.11612/) (2022)

## Abstract (arXiv)

Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that word similarity should be independent of language - i.e., word vectors within one language should not be more similar to each other than to words in another language. We measure this characteristic using modularity, a network measurement that measures the strength of clusters in a graph. Modularity has a moderate to strong correlation with three downstream tasks, even though modularity is based only on the structure of embeddings and does not require any external resources. We show through experiments that modularity can serve as an intrinsic validation metric to improve unsupervised cross-lingual word embeddings, particularly on distant language pairs in low-resource settings.