Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech s Participation in the WMT 2018 Quality Estimation Task
Authors: Eva Martínez García Andoni Azpeitia Zaldua
Date: 30.10.2018
Abstract
We describe Vicomtech’s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators. The core of our approach is based on two simple features: lexical translation overlaps and language model cross-entropy scores. These features are exploited in two system variants: uMQE is an unsupervised system, where the final quality score is obtained by averaging individual feature scores; sMQE is a supervised variant, where the final score is estimated
by a Support Vector Regressor trained on the available annotated datasets. The main goal of our minimalist approach to quality estimation is to provide reliable estimators that require minimal deployment effort, few resources, and, in the case of uMQE, do not depend on costly data annotation or post-editing. Our approach was applied to all language pairs in sentence quality estimation, obtaining competitive results across the board.
BIB_text
title = {Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech s Participation in the WMT 2018 Quality Estimation Task},
pages = {795-800},
keywds = {
Machine Translation, Quality Estimation
}
abstract = {
We describe Vicomtech’s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators. The core of our approach is based on two simple features: lexical translation overlaps and language model cross-entropy scores. These features are exploited in two system variants: uMQE is an unsupervised system, where the final quality score is obtained by averaging individual feature scores; sMQE is a supervised variant, where the final score is estimated
by a Support Vector Regressor trained on the available annotated datasets. The main goal of our minimalist approach to quality estimation is to provide reliable estimators that require minimal deployment effort, few resources, and, in the case of uMQE, do not depend on costly data annotation or post-editing. Our approach was applied to all language pairs in sentence quality estimation, obtaining competitive results across the board.
}
date = {2018-10-30},
}
