Improving Statistical Natural Language Translation with Categories and Rules

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 EnglishGerman VERBMOBIL spontaneous speech corpus.

• Type: Conference
• Year: 1998
• Where: ACL
• Authors: Franz Josef Och, Hans Weber
• Keywords: ACL 1998, ACL 2007, Natural Language Translation, Overall Statistical Approach, Statistical Translation Lexicon