DFKI-LT - Stochastic Parse Tree Selection for an Existing RBMT System

Christian Federmann, Sabine Hunsicker
Stochastic Parse Tree Selection for an Existing RBMT System
in: Chris Callison-Burch, Omar F. Zaidan, Philipp Koehn, Christof Monz (eds.):
2 Sixth Workshop on Statistical Machine Translation, Edinburgh, United Kingdom, Association for Computational Linguistics, EMNLP, 7/2011
 
In this paper we describe our hybrid machine translation system with which we participated in the WMT11 shared translation task. For this, we extended an existing, rule-based MT system with a module for stochastic selection of analysis parse trees that allowed to better cope with parsing errors during the system’s analysis phase. Due to the integration into the analysis phase of the RBMT engine, we are able to preserve the benefits of a rule-based translation system such as proper generation of target language text. Additionally, we used a statistical tool for terminology extraction to improve the lexicon of the RBMT system. We report results from both automated metrics and human evaluation efforts, including examples which show how the proposed approach can improve machine translation quality.
 
Files: BibTeX, wmt11_system.pdf