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The first RBMT systems were developed in types of machine translation pdf early 1970s. Hybrid Machine Translations Systems make use of many principles derived...

The first RBMT systems were developed in types of machine translation pdf early 1970s. Hybrid Machine Translations Systems make use of many principles derived from RBMT. The main approach of RBMT systems is based on linking the structure of the given input sentence with the structure of the demanded output sentence, necessarily preserving their unique meaning. A girl eats an apple.

A dictionary that will map each English word to an appropriate German word. Rules representing regular English sentence structure. Rules representing regular German sentence structure.

And finally, we need rules according to which one can relate these two structures together. Often only partial parsing is sufficient to get to the syntactic structure of the source sentence and to map it onto the structure of the target sentence.

A girl eats an apple. Ein Mädchen isst einen Apfel. TL dictionary – needed by the target language morphological generator to generate target language words.

Target Grammar for the target language which realizes target syntactic constructions as linearized output sentences. No bilingual texts are required. This makes it possible to create translation systems for languages that have no texts in common, or even no digitized data whatsoever. Rules are usually written in a domain independent manner, so the vast majority of rules will “just work” in every domain, and only a few specific cases per domain may need rules written for them.

Every error can be corrected with a targeted rule, even if the trigger case is extremely rare. This is in contrast to statistical systems where infrequent forms will be washed away by default.

Because all rules are hand-written, you can easily debug a rule based system to see exactly where a given error enters the system, and why. Because RBMT systems are generally built from a strong source language analysis that is fed to a transfer step and target language generator, the source language analysis and target language generation parts can be shared between multiple translation systems, requiring only the transfer step to be specialized. Additionally, source language analysis for one language can be reused to bootstrap a closely related language analysis. Insufficient amount of really good dictionaries.

Building new dictionaries is expensive. Some linguistic information still needs to be set manually. It is hard to deal with rule interactions in big systems, ambiguity, and idiomatic expressions. Failure to adapt to new domains.

Although RBMT systems usually provide a mechanism to create new rules and extend and adapt the lexicon, changes are usually very costly and the results, frequently, do not pay off. Computational Model of Grammar for English to Sinhala Machine Translation”. The International Conference on Advances in ICT for Emerging Regions – ICTer20 11 : 026-031. This page was last edited on 14 November 2016, at 03:32.