Maria Holmqvist

Effects of Morphological Analysis in Translation between German and English

We describe the LIU systems for German-English and English-German translation submitted to the Shared Task of the Third Workshop of Statistical Machine Translation. The main features of the systems, as compared with the baseline, is the use of morphological pre- and post-processing, and a sequence model for German using morphologically rich parts-of-speech. It is shown that these additions lead to improved translations.

Generalized syntactic and semantic models of query reformulation

We present a novel approach to query reformulation which combines syntactic and semantic information by means of generalized Levenshtein distance algorithms where the substitution operation costs are based on probabilistic term rewrite functions. We investigate unsupervised, compact and efficient models, and provide empirical evidence of their effectiveness. We further explore a generative model of query reformulation and supervised combination methods providing improved performance at variable computational costs. Among other desirable properties, our similarity measures incorporate information-theoretic interpretations of taxonomic relations such as specification and generalization.

Improving Alignment for SMT by Reordering and Augmenting the Training Corpus

We describe the LIU systems for English-German 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 high-precision 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 part-of-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-of-domain and in-domain data as the mixed corpus had better scores in the baseline configuration.

Getting to Know Moses: Initial Experiments on German-English Factored Translation

We present results and experiences from our experiments with phrase-based statistical machine translation using Moses. The paper is based on the idea of using an off-the-shelf parser to supply linguistic information to a factored translation model and compare the results of German---English translation to the shared task baseline system based on word form. We report partial results for this model and results for two simplified setups. Our best setup takes advantage of the parsers lemmatization and decompounding. A qualitative analysis of compound translation shows that decompounding improves translation quality.