PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Statistical post-editing of a rule-based machine translation system
A. L. Lagarda, V. Alabau, Francisco Casacuberta, R. Silva and E. Díaz de Liaño
In: North American Chapter of the Association for Computational Linguistics - Human Language Technologies, Jun 2009, Boulder, Colorado, USA.

Abstract

Automatic post-editing (APE) systems aim at correcting the output of machine translation systems to produce better quality translations, i.e. produce translations can be manually post-edited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rule-based machine translation (RB) system. In addition, a procedure for effortless human evaluation has been established. We have tested the APE system with two corpora of different complexity. For the Parliament corpus, we show that the APE system significantly complements and improves the RB system. Results for the Protocols corpus, although less conclusive, are promising as well. Finally, several possible sources of errors have been identified which will help develop future system enhancements.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5437
Deposited By:Francisco Casacuberta
Deposited On:08 August 2009