# Cross-lingual Models of Word Embeddings: An Empirical Comparison

arXiv: https://arxiv.org/abs/1604.00425 · HTML: https://mlsanity.com/paper/1604.00425/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2016.

## Built on by (most cited first, 12 of 12)

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- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)
- [TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment](https://mlsanity.com/paper/2605.13429/) (2026)
- [Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph](https://mlsanity.com/paper/2109.04400/) (2021)
- [Mafoko: Structuring and Building Open Multilingual Terminologies for South African NLP](https://mlsanity.com/paper/2508.03529/) (2025)

## Abstract (arXiv)

Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typographically different language pairs. Our evaluation setup spans four different tasks, including intrinsic evaluation on mono-lingual and cross-lingual similarity, and extrinsic evaluation on downstream semantic and syntactic applications. We show that models which require expensive cross-lingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks.