Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation

Abstract

In this paper, we present a two-stage neural quality estimation model that uses multilevel task learning for translation quality estimation (QE) at the sentence, word, and phrase levels. Our approach is based on an end-to-end stacked neural model named Predictor-Estimator, which has two stages consisting of a neural word prediction model and neural QE model. To efficiently train the two-stage model, a stack propagation method is applied, thereby enabling us to jointly learn the word prediction model and QE model in a single learning mode. In addition, we deploy multilevel task learning with stack propagation, where the training examples available for all QE subtasks (i.e., sentence/word/phrase levels) are used to train a Predictor-Estimator for a specific sub-task. All of our submissions to the QE task of WMT17 are ensembles that combine a set of neural models trained under different settings of varying dimensionalities and shuffling training examples, eventually achieving the best performances for all subtasks at the sentence, word, and phrase levels.