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Predictor-Estimator: Neural Quality Estimation Based on Target Word Prediction for Machine Translation by Hyun Kim; Hun-Young Jung; Hongseok Kwon; Jong-Hyeok Lee; Seung-Hoon Na is a Computer Science article available to read on EtoBox.

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Recently, quality estimation has been attracting increasing interest from machine translation researchers, aiming at finding a good estimator for the “quality” of machine translation output. The common approach for quality estimation is to treat the problem as a supervised regression/classification task using a quality-annotated noisy parallel corpus, called __quality estimation data__ , as training data. However, the available size of quality estimation data remains small, due to the too-expensive cost of creating such data. In addition, most conventional quality estimation approaches rely on manually designed features to model nonlinear relationships between feature vectors and corresponding quality labels. To overcome these problems, this article proposes a novel neural network architecture for quality estimation task—called the __predictor-estimator__ —that considers word prediction as an additional pre-task. The major component of the proposed neural architecture is a __word prediction model__ based on a modified neural machine translation model—a probabilistic model for predicting a target word conditioned on all the other source and target contexts. The underlying assumption

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Author

Hyun Kim; Hun-Young Jung; Hongseok Kwon; Jong-Hyeok Lee; Seung-Hoon Na

Publisher

ACM

Published

2017

Language

EN

Field

Computer Science (Physical Sciences)