  We translate a closed text that is known in advance into a severely low
resource language by leveraging massive source parallelism. In other words,
given a text in 124 source languages, we translate it into a severely low
resource language using only ~1,000 lines of low resource data without any
external help. Firstly, we propose a systematic method to rank and choose
source languages that are close to the low resource language. We call the
linguistic definition of language family Family of Origin (FAMO), and we call
the empirical definition of higher-ranked languages using our metrics Family of
Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual
Order-preserving Lexiconized Transformer (IPML) to train on ~1,000 lines
(~3.5%) of low resource data. To translate named entities correctly, we build a
massive lexicon table for 2,939 Bible named entities in 124 source languages,
and include many that occur once and covers more than 66 severely low resource
languages. Moreover, we also build a novel method of combining translations
from different source languages into one. Using English as a hypothetical low
resource language, we get a +23.9 BLEU increase over a multilingual baseline,
and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We
get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA
dataset. We also have good results for a real severely low resource Mayan
language, Eastern Pokomchi.
