Paper: Improving Statistical Natural Language Translation with Categories and Rules

ACL ID C98-2157
Title Improving Statistical Natural Language Translation with Categories and Rules
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998
Authors

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.