Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Parallel FDA5 for fast deployment of accurate statistical machine translation systems

Bicici, Ergun, Liu, Qun and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2014) Parallel FDA5 for fast deployment of accurate statistical machine translation systems. In: ACL 2014, Ninth Workshop on Statistical MachineTranslation, 26-27 June, 2014, Baltimore, USA.

Abstract
We use parallel FDA5, an efficiently parameterized and optimized parallel implementation of feature decay algorithms for fast deployment of accurate statistical machine translation systems, taking only about half a day for each translation direction. We build Parallel FDA5 Moses SMT systems for all language pairs in the WMT14 translation task and obtain SMT performance close to the top Moses systems with an average of $3.49$ BLEU points difference using significantly less resources for training and development.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of ACL 2014 NINTH WORKSHOP ON STATISTICAL MACHINE TRANSLATION. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://www.statmt.org/wmt14/papers.html
Copyright Information:© 2014 ACL
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7 QTLaunchPad project, CNGL Centre for Global Intelligent Content, Science Foundation Ireland
ID Code:19989
Deposited On:11 Jul 2014 13:00 by Mehmet Ergun Bicici . Last Modified 09 Nov 2018 14:22
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record