Type

Conference Proceedings

Authors

Andy Way
Qun Liu
Mehmet Ergun Bicici

Subjects

Linguistics

Topics
language pairs machine translation systems deployment statistics machine translating statistical machine translation feature decay algorithms information retrieval

ParFDA for fast deployment of accurate statistical machine translation systems, benchmarks, and statistics (2015)

Abstract We build parallel FDA5 (ParFDA) Moses statistical machine translation (SMT) systems for all language pairs in the workshop on statistical machine translation (Bojar et al., 2015) (WMT15) translation task and obtain results close to the top with an average of 3.176 BLEU points difference using significantly less resources for building SMT systems. ParFDA is a parallel implementation of feature decay algorithms (FDA) developed for fast deployment of accurate SMT systems. ParFDA Moses SMT system we built is able to obtain the top TER performance in French to English translation. We make the data for building ParFDA Moses SMT systems for WMT15 available: \url{https://github.com/bicici/ParFDAWMT15}.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Next Generation Localisation (CNGL)
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> Subject = Computer Science: Information retrieval

Full list of authors on original publication

Andy Way, Qun Liu, Mehmet Ergun Bicici

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
Qun Liu
Dublin City University
Total Publications: 31