The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Unit completion for a computer-aided translation typing system
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A comparison of alignment models for statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Towards a unified approach to memory- and statistical-based machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A maximum entropy/minimum divergence translation model
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
HLT '93 Proceedings of the workshop on Human Language Technology
Toward hierarchical models for statistical machine translation of inflected languages
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
An intelligent terminology database as a pre-processor for statistical machine translation
COMPUTERM '02 COLING-02 on COMPUTERM 2002: second international workshop on computational terminology - Volume 14
| Hi-index | 0.00 |
The past decade has witnessed exciting work in the field of Statistical Machine Translation (SMT). However, accurate evaluation of its potential in real-life contexts is still a questionable issue.In this study, we investigate the behavior of an SMT engine faced with a corpus far different from the one it has been trained on. We show that terminological databases are obvious resources that should be used to boost the performance of a statistical engine. We propose and evaluate a way of integrating terminology into a SMT engine which yields a significant reduction in word error rate.