On the Use of Grammar Based Language Models for Statistical Machine Translation (1999)
| Venue: | 6th Int. Workshop on Parsing Technologies |
| Citations: | 6 - 5 self |
BibTeX
@INPROCEEDINGS{Sawaf99onthe,
author = {Hassan Sawaf and Kai Schütz and Hermann Ney and Lehrstuhl Fur Informatik Vi},
title = {On the Use of Grammar Based Language Models for Statistical Machine Translation},
booktitle = {6th Int. Workshop on Parsing Technologies},
year = {1999},
pages = {231--241},
publisher = {ACL/SIGPARSE}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
In this paper, we describe some concepts of language models beyond the usually used standard trigram and prove the need of such language models for statistical machine translation. In statistical machine translation the language model is the a-priori knowledge source of the system about the target language. The most important demands for the language model in statistical machine translation is the correct word order, given a certain choice of words, and to score the selection of translations, that are done by the translation model Pr(f J 1 je I 1 ), in view of the syntactical context. Beside the inquisition of standard m-grams with long histories, we examined the use of Part-of-Speech based models as well as linguistically motivated grammars with stochastic parsing as a special type of language model. Translation results are given on the Verbmobil task, where translation are performed from German to English, with vocabulary sizes of 6500 and 4000 words respectively. 1 Introduct...
