Paper Info
Title
Direct Translation Model 2
Abstract
This paper presents a maximum entropy ma- chine translation system using a minimal set of translation blocks (phrase-pairs). While recent phrase-based statistical machine trans- lation (SMT) systems achieve significant im- provement over the original source-channel sta- tistical translation models, they 1) use a large inventory of blocks which have significant over- lap and 2) limit the use of training to just a few parameters (on the order of ten). In con- trast, we show that our proposed minimalist system (DTM2) achieves equal or better per- formance by 1) recasting the translation prob- lem in the traditional statistical modeling ap- proach using blocks with no overlap and 2) re- lying on training most system parameters (on the order of millions or larger). The new model is a direct translation model (DTM) formu- lation which allows easy integration of addi- tional/alternative views of both source and tar- get sentences such as segmentation for a source language such as Arabic, part-of-speech of both source and target, etc. We show improvements over a state-of-the-art phrase-based decoder in Arabic-English translation.
Year
Venue
Keywords
2007
HLT-NAACL
statistical model,part of speech,maximum entropy
Field
DocType
Citations 
Rule-based machine translation,Example-based machine translation,Segmentation,Computer science,Machine translation,Phrase,Natural language processing,Artificial intelligence,Transfer-based machine translation,Statistical model,Principle of maximum entropy
Conference
26
PageRank 
References 
Authors
1.30
2
2
Name
Order
Citations
PageRank
Abraham Ittycheriah153461.23
Salim Roukos26248845.50