This paper presents a set of effective approaches to handle extremely low-resource
language pairs for self-attention based neural machine translation (NMT) focusing on
English and four Asian languages. Starting from an initial set of parallel sentences used to
train bilingual baseline models, we introduce additional monolingual corpora and data
processing techniques to improve translation quality. We describe a series of best practices
and empirically validate the methods through an evaluation conducted on eight translation …