The UPC-lsi Discriminative Phrase Selection System: NIST MT Evaluation 2008
Cristina Espa · 2008
This document describes the system developed by the Empirical MT Group at the Technical University of Catalonia, LSI Department, for the Arabic-to-English task at the 2008 NIST MT Evaluation Campaign. Our system explores the application of discriminative learning to the problem of phrase selection in Statistical Machine Translation. Instead of relying on Maximum Likelihood estimates for the construction of translation models, we use local classifiers which are able to take further advantage of contextual information. Local predictions are softly integrated into a global log-linear phrase-based statistical MT system as an additional feature. Automatic evaluation results according to a heterogeneous set of metrics operating at different linguistic levels are presented. These show a low level of agreement between metrics. Improvements over the baseline are either inexistent or not significant, except for the case of semantic metrics based on discourse representations and several syntactic metrics based on constituent and dependency parsing.