  A common bottleneck for developing machine translation (MT) systems for some
language pairs is the lack of direct parallel translation data sets, in general
and in certain domains. Alternative solutions such as zero-shot models or
pivoting techniques are successful in getting a strong baseline, but are often
below the more supported language-pair systems. In this paper, we focus on
Arabic-Japanese machine translation, a less studied language pair; and we work
with a unique parallel corpus of Arabic news articles that were manually
translated to Japanese. We use this parallel corpus to adapt a state-of-the-art
domain/genre agnostic neural MT system via a simple automatic post-editing
technique. Our results and detailed analysis suggest that this approach is
quite viable for less supported language pairs in specific domains.
