Type

Conference Proceedings

Authors

Andy Way
Antal van den Bosch
Nicolas Stroppa

Subjects

Linguistics

Topics
state of the art chinese translation statistical machine translation smt machine translating classification source phrase based smt

Exploiting source similarity for SMT using context-informed features (2007)

Abstract In this paper, we introduce context informed features in a log-linear phrase-based SMT framework; these features enable us to exploit source similarity in addition to target similarity modeled by the language model. We present a memory-based classification framework that enables the estimation of these features while avoiding sparseness problems. We evaluate the performance of our approach on Italian-to-English and Chinese-to-English translation tasks using a state-of-the-art phrase-based SMT system, and report significant improvements for both BLEU and NIST scores when adding the context-informed features.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: National Centre for Language Technology (NCLT)
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres

Full list of authors on original publication

Andy Way, Antal van den Bosch, Nicolas Stroppa

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
Antal van den Bosch
Dublin City University
Total Publications: 5