A Universal Parent Model for Low-Resource Neural Machine Translation Transfer

Understand

Transfer learning from a high-resource language pair `parent' has been proven to be an effective way to improve neural machine translation quality for low-resource language pairs `children.' However, previous approaches build a custom parent model or at least update an existing parent model's vocabulary for each child language pair they wish to train, in an effort to align parent and child vocabularies.

• This is not a practical solution.
• It is wasteful to devote the majority of training time for new language pairs to optimizing parameters on an unrelated data set.
• Further, this overhead reduces the utility of neural machine translation for deployment in humanitarian assistance scenarios, where extra time to deploy a new language pair can mean the difference between life and death.