Publikation
Stochastic Parse Tree Selection for an Existing RBMT System
Christian Federmann; Sabine Hunsicker
In: Chris Callison-Burch; Omar F. Zaidan; Philipp Koehn; Christof Monz (Hrsg.). Sixth Workshop on Statistical Machine Translation. Workshop on Statistical Machine Translation (WMT-11), located at EMNLP, July 27-31, Edinburgh, United Kingdom, Association for Computational Linguistics, 7/2011.
Zusammenfassung
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.