Paper Info
Title
Approximating context-free by rational transduction for example-based MT
Abstract
Existing studies show that a weighted context-free transduction of reasonable quality can be effectively learned from examples. This paper investigates the approximation of such transduction by means of weighted rational transduction. The advantage is increased processing speed, which benefits real-time applications involving spoken language.
Year
DOI
Venue
2001
10.3115/1118037.1118041
DDMMT@ACL
Keywords
Field
DocType
approximating context-free,weighted rational transduction,weighted context-free transduction,example-based mt,real-time application,reasonable quality
Computer science,Artificial intelligence,Transduction (genetics),Spoken language,Machine learning
Conference
Volume
Citations 
PageRank 
W01-14
0
0.34
References 
Authors
10
1
Name
Order
Citations
PageRank
Mark-jan Nederhof138753.30