Using Interlinear Glosses as Pivot in Low Resource Multilingual Machine Translation.
We demonstrate a new approach to Neural Machine Translation (NMT) for low resource languages using a ubiquitous linguistic resource, Interlinear Glossed Text (IGT) IGT represents a non English sentence as a sequence of English lemmas and morpheme labels. As such, it can serve as a pivot or interlingua for NMT. Our contribution is four fold. Firstly, we pool IGT for 1,497 languages in ODIN (54,545 glosses) and 70,918 glosses in Arapaho and train a gloss to target NMT system from IGT to English, with a BLEU score of 25.94. We introduce a multilingual NMT model that tags all glossed text with gloss source language tags and train a universal system with shared attention across 1,497 languages. Secondly, we use the IGT gloss to target translation as a key step in an English Turkish MT system trained on only 865 lines from ODIN. Thirdly, we we present five metrics for evaluating extremely low resource translation when BLEU is no longer sufficient and evaluate the Turkish low resource system using BLEU and also using accuracy of matching nouns, verbs, agreement, tense, and spurious repetition, showing large improvements.