Deep grammars in a tree labeling approach to syntax-based statistical machine translation

Authors:
Mark Hopkins;Jonas Kuhn
Affiliations:
University of Potsdam, Germany;University of Potsdam, Germany
Venue:
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Year:
2007

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Abstract

In this paper, we propose a new syntaxbased machine translation (MT) approach based on reducing the MT task to a tree-labeling task, which is further decomposed into a sequence of simple decisions for which discriminative classifiers can be trained. The approach is very flexible and we believe that it is particularly well-suited for exploiting the linguistic knowledge encoded in deep grammars whenever possible, while at the same time taking advantage of data-based techniques that have proven a powerful basis for MT, as recent advances in statistical MT show. A full system using the Lexical-Functional Grammar (LFG) parsing system XLE and the grammars from the Parallel Grammar development project (ParGram; (Butt et al., 2002)) has been implemented, and we present preliminary results on English-to-German translation with a tree-labeling system trained on a small subsection of the Europarl corpus.