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\%) 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  .
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 .
R
Our contribution is four-fold
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
R
rank 124 source languages empirically to determine their closeness to
propose a systematic method to rank and choose source languages that are close to
D
and select the top few
R
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
To translate named entities correctly, we
R
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
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
A
. We also have good results for a real severely low resource Mayan language, Eastern Pokomchi