QUALES: Machine translation quality estimation via supervised and unsupervised machine learning [QUALES: Estimación Automática de Calidad de Traducción Mediante Aprendizaje Automático Supervisado y No-Supervisado]
Authors: Eva Martínez García Andoni Azpeitia Zaldua Iñaki Alegria Gorka Labaka Arantza Otegi Kepa Sarasola Itziar Cortes Amaia Jauregi Igor Ellakuria Eusebi Calonge Maite Martín
Date: 01.09.2018
Procesamiento de Lenguaje Natural
Abstract
The automatic quality estimation (QE) of machine translation consists in measuring the quality of translations without access to human references, usually via machine learning approaches. A good QE system can help in three aspects of translation processes involving machine translation and post-editing: increasing productivity (by ruling out poor quality machine translation), estimating costs (by helping to forecast the cost of post-editing) and selecting a provider (if several machine translation systems are available). Interest in this research area has grown significantly in recent years, leading to regular shared tasks in the main machine translation conferences and intense scientific activity. In this article we review the state of the art in this research area and present project QUALES, which is under development. © 2018 Sociedad Española para el Procesamiento del Lenguaje Natural.
BIB_text
title = {QUALES: Machine translation quality estimation via supervised and unsupervised machine learning [QUALES: Estimación Automática de Calidad de Traducción Mediante Aprendizaje Automático Supervisado y No-Supervisado]},
journal = {Procesamiento de Lenguaje Natural},
pages = {143-146},
volume = {61},
abstract = {
The automatic quality estimation (QE) of machine translation consists in measuring the quality of translations without access to human references, usually via machine learning approaches. A good QE system can help in three aspects of translation processes involving machine translation and post-editing: increasing productivity (by ruling out poor quality machine translation), estimating costs (by helping to forecast the cost of post-editing) and selecting a provider (if several machine translation systems are available). Interest in this research area has grown significantly in recent years, leading to regular shared tasks in the main machine translation conferences and intense scientific activity. In this article we review the state of the art in this research area and present project QUALES, which is under development. © 2018 Sociedad Española para el Procesamiento del Lenguaje Natural.
}
date = {2018-09-01},
}
