Learning word embeddings using distributional information is a task that has been studied by many researchers, and a lot of studies are reported in the literature. On the contrary, less studies were done for the case of multiple languages. The idea is to focus on a single representation for a pair of languages such that semantically similar words are closer to one another in the induced representation irrespective of the language. In this way, when data are missing for a particular language, classifiers from another language can be used.
@article{arxiv.1912.10167,
title = {Machine Translation with Cross-lingual Word Embeddings},
author = {Marco Berlot and Evan Kaplan},
journal= {arXiv preprint arXiv:1912.10167},
year = {2020}
}