We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. Our contribution is four-fold. Firstly, we rank 124 source languages empirically to determine their closeness to the low resource language and select the top few. 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 %DIFDELCMD < \\%%% %DIF < ) of low resource data from the Bible dataset and the medical EMEA dataset. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. 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. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.abstract 
We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. Our contribution is four-fold. Firstly, we rank 124 source languages empirically to determine their closeness to the low resource language and select the top few. 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 %DIFDELCMD < \\%%% %DIF < ) of low resource data from the Bible dataset and the medical EMEA dataset. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. 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. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.abstract \%) of low resource data. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline in the Bible dataset. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. 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. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.
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\%) of low resource data. Using English as a hypothetical low resource language to translate from Spanish, we obtain a +24.7 BLEU increase over a multilingual baseline, and a +10.2 BLEU increase over our asymmetric baseline in the Bible dataset. Thirdly, we also use a real severely low resource Mayan language, Eastern Pokomchi. Finally, we add an order-preserving lexiconized component to translate named entities accurately. 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. Training on randomly sampled 1,093 lines of low resource data, we reach a 30.3 BLEU score for Spanish-English translation testing on 30,022 lines of Bible, and a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.