  It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, often requiring large amounts of
auxiliary data to achieve competitive results. An effective method of
generating auxiliary data is back-translation of target language sentences. In
this work, we present a case study of Tigrinya where we investigate several
back-translation methods to generate synthetic source sentences. We find that
in low-resource conditions, back-translation by pivoting through a
higher-resource language related to the target language proves most effective
resulting in substantial improvements over baselines.
