Paper Info
Title
Enriching Morphologically Poor Languages for Statistical Machine Translation
Abstract
We address the problem of translating from morphologically poor to morphologically rich languages by adding per-word linguistic in- formation to the source language. We use the syntax of the source sentence to extract information for noun cases and verb persons and annotate the corresponding words accord- ingly. In experiments, we show improved performance for translating from English into Greek and Czech. For English-Greek, we re- duce the error on the verb conjugation from 19% to 5.4% and noun case agreement from 9% to 6%.
Year
Venue
Keywords
2008
ACL
noun
Field
DocType
Volume
Rule-based machine translation,Verb,Czech,Computer science,Noun,Machine translation,Artificial intelligence,Natural language processing,Syntax,Sentence,Linguistics
Conference
P08-1
Citations 
PageRank 
References 
15
0.67
17
Authors
2
Name
Order
Citations
PageRank
Eleftherios Avramidis18418.17
Philipp Koehn27684431.77