  We translate a closed text that is known in advance and available in many
languages into a new and severely low resource language. Most human translation
efforts adopt a portion-based approach to translate consecutive pages/chapters
in order, which may not suit machine translation. We compare the portion-based
approach that optimizes coherence of the text locally with the random sampling
approach that increases coverage of the text globally. Our results show that
the random sampling approach performs better. When training on a seed corpus of
~1,000 lines from the Bible and testing on the rest of the Bible (~30,000
lines), random sampling gives a performance gain of +11.0 BLEU using English as
a simulated low resource language, and +4.9 BLEU using Eastern Pokomchi, a
Mayan language. Furthermore, we compare three ways of updating machine
translation models with increasing amount of human post-edited data through
iterations. We find that adding newly post-edited data to training after
vocabulary update without self-supervision performs the best. We propose an
algorithm for human and machine to work together seamlessly to translate a
closed text into a severely low resource language.
