  Idioms pose problems to almost all Machine Translation systems. This type of
language is very frequent in day-to-day language use and cannot be simply
ignored. The recent interest in memory augmented models in the field of
Language Modelling has aided the systems to achieve good results by bridging
long-distance dependencies. In this paper we explore the use of such techniques
into a Neural Machine Translation system to help in translation of idiomatic
language.
