  We present a language independent, unsupervised approach for transforming
word embeddings from source language to target language using a transformation
matrix. Our model handles the problem of data scarcity which is faced by many
languages in the world and yields improved word embeddings for words in the
target language by relying on transformed embeddings of words of the source
language. We initially evaluate our approach via word similarity tasks on a
similar language pair - Hindi as source and Urdu as the target language, while
we also evaluate our method on French and German as target languages and
English as source language. Our approach improves the current state of the art
results - by 13% for French and 19% for German. For Urdu, we saw an increment
of 16% over our initial baseline score. We further explore the prospects of our
approach by applying it on multiple models of the same language and
transferring words between the two models, thus solving the problem of missing
words in a model. We evaluate this on word similarity and word analogy tasks.
