Towards hybrid quality-oriented machine translation - on linguistics and probabilities in MT.

Abstract

We present a hybrid MT architecture, combining state-of-the-art linguistic processing with advanced stochastic techniques. Grounded in a theoretical reflection on the division of labor between rule-based and probabilistic elements in the MT task, we summarize per-component approaches to ranking, including empirical results when evaluated in isolation. Combining component-internal scores and a number of additional sources of (probabilistic) information, we explore discriminative re-ranking of n-best lists of candidate translations through an eclectic combination of knowledge sources, and provide evaluation results for various configurations.