We describe the LIU systems for EnglishGerman and German-English translation in the WMT09 shared task. We focus on two methods to improve the word alignment: (i) by applying Giza++ in a second phase to a reordered training corpus, where reordering is based on the alignments from the first phase, and (ii) by adding lexical data obtained as highprecision alignments from a different word aligner. These methods were studied in the context of a system that uses compound processing, a morphological sequence model for German, and a partof-speech sequence model for English. Both methods gave some improvements to translation quality as measured by Bleu and Meteor scores, though not consistently. All systems used both out-ofdomain and in-domain data as the mixed corpus had better scores in the baseline configuration.
REF also reported that using POS-based and morphologybased sequence model would give an improvement to the translation quality between English and German in WMT09 shared task.
Improving Alignment for SMT by Reordering and Augmenting the Training Corpus
Proceedings of the Fourth Workshop on Statistical Machine Translation
Computer Science