PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating the Sentence-Level Quality of Machine Translation Systems
Lucia Specia, Marco Turchi, Nicola Cancedda, Marc Dymetman and Nello Cristianini
EAMT 2009.

Abstract

We investigate the problem of predicting the quality of sentences produced by ma- chine translation systems when reference translations are not available. The prob- lem is addressed as a regression task and a method that takes into account the con- tribution of different features is proposed. We experiment with this method for trans- lations produced by various MT systems and different language pairs, annotated with quality scores both automatically and manually. Results show that our method allows obtaining good estimates and that identifying a reduced set of relevant fea- tures plays an important role. The experi- ments also highlight a number of outstand- ing features that were consistently selected as the most relevant and could be used in different ways to improve MT perfor- mance or to enhance MT evaluation.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:5870
Deposited By:Marc Dymetman
Deposited On:08 March 2010