Latest trends in hybrid machine translation and its applications

This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type. This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowl- edge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.Peer ReviewedPostprint (published version

Lexical Selection for Hybrid MT with Sequence Labeling

Abstract We present initial work on an inexpensive approach for building largevocabulary lexical selection modules for hybrid RBMT systems by framing lexical selection as a sequence labeling problem. We submit that Maximum Entropy Markov Models (MEMMs) are a sensible formalism for this problem, due to their ability to take into account many features of the source text, and show how we can build a combination MEMM/HMM system that allows MT system implementors flexibility regarding which words have their lexical choices modeled with classifiers. We present initial results showing successful use of this system both in translating English to Spanish and Spanish to Guarani