We introduce a word alignment framework that facilitates the incorporation of syntax encoded in bilingual dependency tree pairs. Our model consists of two sub-models: an anchor word alignment model which aims to find a set of high-precision anchor links and a syntaxenhanced word alignment model which focuses on aligning the remaining words relying on dependency information invoked by the acquired anchor links. We show that our syntaxenhanced word alignment approach leads to a 10.32% and 5.57% relative decrease in alignment error rate compared to a generative word alignment model and a syntax-proof discriminative word alignment model respectively. Furthermore, our approach is evaluated extrinsically using a phrase-based statistical machine translation system. The results show that SMT systems based on our word alignment approach tend to generate shorter outputs. Without length penalty, using our word alignments yields statistically significant improvement in Chinese-English machine translation in comparison with the baseline word alignment.

In this paper, we give a description of the machine translation system developed at DCU that was used for our participation in the evaluation campaign of the Third Workshop on Statistical Machine Translation at ACL 2008.We describe the modular design of our datadriven MT system with particular focus on the components used in this participation. We also describe some of the significant modules which were unused in this task.We participated in the EuroParl task for the following translation directions: SpanishEnglish and French-English, in which we employed our hybrid EBMT-SMT architecture to translate. We also participated in the CzechEnglish News and News Commentary tasks which represented a previously untested language pair for our system. We report results on the provided development and test sets.

This paper presents the first step to project POS tags and dependencies from English and French to Polish in aligned corpora. Both the English and French parts of the corpus are analysed with a POS tagger and a robust parser. The English/Polish bi-text and the French/Polish bi-text are then aligned at the word level with the GIZA++ package. The intersection of IBM-4 Viterbi alignments for both translation directions is used to project the annotations from English and French to Polish. The results show that the precision of direct projection vary according to the type of induced annotations as well as the source language. Moreover, the performances are likely to be improved by defining regular conversion rules among POS tags and dependencies.

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We present a word alignment procedure based on a syntactic dependency analysis of French/English parallel corpora called "alignment by syntactic propagation". Both corpora are analysed with a deep and robust parser. Starting with an anchor pair consisting of two words which are potential translations of one another within aligned sentences, the alignment link is propagated to the syntactically connected words. The method was tested on two corpora and achieved a precision of 94.3 and 93.1% as well as a recall of 58 and 56%, respectively for each corpus.