A Distributed Resource Repository for Cloud-Based Machine Translation

TY - GEN

T1 - A Distributed Resource Repository for Cloud-Based Machine Translation

AU - Tiedemann, Jörg

AU - Hansen, Dorte Haltrup

AU - Offersgaard, Lene

AU - Olsen, Sussi

AU - Zumpe, Matthias

N1 - Conference code: 8

PY - 2012

Y1 - 2012

N2 - In this paper, we present the architecture of a distributed resource repository developed for collecting training data for building customized statistical machine translation systems. The repository is designed for the cloud-based translation service integrated in the Let'sMT! platform which is about to be launched to the public. The system includes important features such as automatic import and alignment of textual documents in a variety of formats, a flexible database for meta-information using modern key-value stores and a grid-based backend for running off-line processes. The entire system is very modular and supports highly distributed setups to enable a maximum of flexibility and scalability. The system uses secure connections and includes an effective permission management to ensure data integrity. In this paper, we also take a closer look at the task of sentence alignment. The process of alignment is extremely important for the success of translation models trained on the platform. Alignment decisions significantly influence the quality of SMT engines.

AB - In this paper, we present the architecture of a distributed resource repository developed for collecting training data for building customized statistical machine translation systems. The repository is designed for the cloud-based translation service integrated in the Let'sMT! platform which is about to be launched to the public. The system includes important features such as automatic import and alignment of textual documents in a variety of formats, a flexible database for meta-information using modern key-value stores and a grid-based backend for running off-line processes. The entire system is very modular and supports highly distributed setups to enable a maximum of flexibility and scalability. The system uses secure connections and includes an effective permission management to ensure data integrity. In this paper, we also take a closer look at the task of sentence alignment. The process of alignment is extremely important for the success of translation models trained on the platform. Alignment decisions significantly influence the quality of SMT engines.

M3 - Article in proceedings

SN - 978-2-9517408-7-7

SP - 2207

EP - 2213

BT - Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

PB - European Language Resources Association

CY - Istanbul,Tyrkiet

T2 - International Conference on Language Resources and Evaluation

Y2 - 23 May 2012 through 25 May 2012

ER -