MINIMIZATION OF COMPUTATIONAL DEMANDS IN MODEL AGNOSTIC CROSS-LINGUAL TRANSFER WITH NEURAL TASK REPRESENTATIONS AS WEAK SUPERVISION

摘要

A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.