  Low-resource Multilingual Neural Machine Translation (MNMT) is typically
tasked with improving the translation performance on one or more language pairs
with the aid of high-resource language pairs. In this paper, we propose two
simple search based curricula -- orderings of the multilingual training data --
which help improve translation performance in conjunction with existing
techniques such as fine-tuning. Additionally, we attempt to learn a curriculum
for MNMT from scratch jointly with the training of the translation system with
the aid of contextual multi-arm bandits. We show on the FLORES low-resource
translation dataset that these learned curricula can provide better starting
points for fine tuning and improve overall performance of the translation
system.
