Improving statistical natural language translation with categories and rules

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 generalized form of a word alignment (WA). The translation process itself is realized as a beam search. In our method example-based techniques enter an overall statistical approach leading to about 50 percent correctly translated sentences applied to the very difficult English-German VERBMOBIL spontaneous speech corpus.