PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

GREAT: a finite-state machine translation toolkit implementing a Grammatical Inference Approach for Transducer Inference (GIATI)
J. González and Francisco Casacuberta
In: EACL Workshop on Computational Linguistics Aspects of Grammatical Inference, March 30, 2009, Athens, Greece.

Abstract

GREAT is a finite-state toolkit which is devoted to Machine Translation and that learns structured models from bilingual data. The training procedure is based on grammatical inference techniques to obtain stochastic transducers that model both the structure of the languages and the relationship between them. The inference of grammars from natural language causes the models to become larger when a less restrictive task is involved; even more if a bilingual modelling is being considered. GREAT has been successful to implement the GIATI learning methodology, using different scalability issues to be able to deal with corpora of high volume of data. This is reported with experiments on the EuroParl corpus, which is a state-of-theart task in Statistical Machine Translation.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
ID Code:5439
Deposited By:Francisco Casacuberta
Deposited On:08 August 2009