Improving Statistical Natural Language Translation with Categories and Rules (1998)
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| Venue: | in Proc. of the 35th Annual Conf. of the Association for Computational Linguistics and the 17th Int. Conf. on Computational Linguistics |
| Citations: | 11 - 3 self |
BibTeX
@INPROCEEDINGS{Och98improvingstatistical,
author = {Franz Josef Och and Hans Weber},
title = {Improving Statistical Natural Language Translation with Categories and Rules},
booktitle = {in Proc. of the 35th Annual Conf. of the Association for Computational Linguistics and the 17th Int. Conf. on Computational Linguistics},
year = {1998},
pages = {985--989}
}
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Abstract
This paper describes an all level approach on statistical natural language translation (SNLT). Without any predefined knowledge the system learns a statistical translation lexicon (STL), word classes (WCs) and translation rules (TRs) from a parallel corpus thereby producing a gen-eralized form of a word alignment (WA). The translation process itself is realized as a beam search. In our method example-based tech-niques enter an overall statistical approach lead-ing to about 50 percent correctly translated sentences applied to the very ditficult English-German VERBMOBIL spontaneous speech cor-pus. 1
