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

Language-independent hybrid mt with presemt

Abstract The present article provides a comprehensive review of the work carried out on developing PRESEMT, a hybrid language-independent machine translation (MT) methodology. This methodology has been designed to facilitate rapid creation of MT systems for unconstrained language pairs, setting the lowest possible requirements on specialised resources and tools. Given the limited availability of resources for many languages, only a very small bilingual corpus is required, while language modelling is performed by sampling a large target language (TL) monolingual corpus. The article summarises implementation decisions, using the Greek-English language pair as a test case. Evaluation results are reported, for both objective and subjective metrics. Finally, main error sources are identified and directions are described to improve this hybrid MT methodology