Stochastic Parse Tree Selection for an Existing RBMT System

Abstract

In this paper we describe our hybrid machine translation system with which we participated in the WMT11 shared translation task for the English→German language pair. Our system was able to outperform its RBMT baseline and turned out to be the best-scored participating system in the manual evaluation. To achieve 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. 1