Paper Info
Title
A Multi-Genre SMT System for Arabic to French
Abstract
This work presents improvements of a large-scale Arabic to French statistical machine translation system over a period of three years. The development includes better preprocessing, more training data, additional genre-specific tuning for different domains, namely newswire text and broadcast news transcripts, and improved domain-dependent language models. Starting with an early prototype in 2005 that participated in the second CESTA evaluation, the system was further upgraded to achieve favorable BLEU scores of 44.8% for the text and 41.1% for the audio setting. These results are compared to a system based on the freely available Moses toolkit. We show significant gains both in terms of translation quality (up to +1.2% BLEU absolute) and translation speed (up to 16 times faster) for comparable configuration settings.
Year
Venue
Keywords
2008
SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008
language model
Field
DocType
Citations 
Broadcasting,Example-based machine translation,BLEU,Arabic,Computer science,Machine translation,Speech recognition,Machine translation software usability,Preprocessor,Artificial intelligence,Natural language processing,Language model
Conference
2
PageRank 
References 
Authors
0.35
7
2
Name
Order
Citations
PageRank
Sasa Hasan124517.35
Hermann Ney2141781506.93