Improving Alignment for SMT by Reordering and Augmenting the Training Corpus


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.

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@inproceedings{Holmqvist2009ImprovingAF, title={Improving Alignment for SMT by Reordering and Augmenting the Training Corpus}, author={Maria Holmqvist and Sara Stymne and Jody Foo and Lars Ahrenberg}, booktitle={WMT@EACL}, year={2009} }