Alignment templates: the RWTH SMT system

In this paper, we describe the RWTH statistical machine translation (SMT) system which is based on log-linear model combination. All knowledge sources are treated as feature functions which depend on the source language sentence, the target language sentence and possible hidden variables. The main feature of our approach are the alignment templates which take shallow phrase structures into account: a phrase level alignment between phrases and a word level alignment between single words within the phrases. Thereby, we directly consider word contexts and local reorderings. In order to incorporate additional models (the IBM-1 statistical lexicon model, a word deletion model, and higher order language models), we perform n-best list rescoring. Participating in the International Workshop on Spoken Language Translation (IWSLT 2004), we evaluate our system on the Basic Travel Expression Corpus (BTEC) Chinese-to-English and Japanese-to-English tasks.