Machine Translation with Cross-lingual Word Embeddings

TL;DR

This paper explores the use of cross-lingual word embeddings to create a shared semantic space for multiple languages, enabling better transfer learning and machine translation when data for some languages is limited.

Contribution

It introduces a method for learning a unified word embedding space across languages, facilitating cross-lingual tasks and improving translation quality.

Findings

Cross-lingual embeddings align semantically similar words across languages

Shared representations enable transfer learning in low-resource languages

Potential improvements in machine translation accuracy

Abstract

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.