  Neural Machine Translation (NMT) models have been effective on large
bilingual datasets. However, the existing methods and techniques show that the
model's performance is highly dependent on the number of examples in training
data. For many languages, having such an amount of corpora is a far-fetched
dream. Taking inspiration from monolingual speakers exploring new languages
using bilingual dictionaries, we investigate the applicability of bilingual
dictionaries for languages with extremely low, or no bilingual corpus. In this
paper, we explore methods using bilingual dictionaries with an NMT model to
improve translations for extremely low resource languages. We extend this work
to multilingual systems, exhibiting zero-shot properties. We present a detailed
analysis of the effects of the quality of dictionaries, training dataset size,
language family, etc., on the translation quality. Results on multiple
low-resource test languages show a clear advantage of our bilingual
dictionary-based method over the baselines.
