  It is very challenging to work with low-resource languages due to the
inadequate availability of data. Using a dictionary to map independently
trained word embeddings into a shared vector space has proved to be very useful
in learning bilingual embeddings in the past. Here we have tried to map
individual embeddings of words in English and their corresponding translated
words in low-resource languages like Estonian, Slovenian, Slovakian, and
Hungarian. We have used a supervised learning approach. We report accuracy
scores through various retrieval strategies which show that it is possible to
approach challenging tasks in Natural Language Processing like machine
translation for such languages, provided that we have at least some amount of
proper bilingual data. We also conclude that we can follow an unsupervised
learning path on monolingual text data as that is more suitable for
low-resource languages.
