International Journal of Engineering and Technology

Machine Translation is an essential approach for localization, and is especially appropriate in a linguistically diversenation like India. Automatic translation between languages which are morphologically rich and syntactically different is generally regarded as a complex task. A number of machine translation systems have been proposed in literature. But, conventional rule-based machine translation system is costly in terms of formulating rules. It introduces inconsistencies, and it is inflexible to be robust. Statistical MT is an approach that automatically attains knowledge from a vast amount of training data. This approach is characterized by the use of machine learning techniques. But, still there is scope for better performance of the system. In this paper, a Hybrid Machine Translation (HMT) approach is proposed which is the combination of rule based and statistical technique for translating text from English to Indian languages such as Tamil, Malayalam and Hindi. The rule based machine translation technique, involves the formation of rules which helps to re-order the syntactic structures of the source language sentence along with its dependency information which brings close to the structure of the target sentence. The parser identifies the syntactical elements in English sentences and suggests its Indian languages translation taking into account various grammatical forms of those Indian languages. Context Free Grammars (CFG) is used in generation of the language structures, and then the errors in the translated sentences are corrected by applying a statistical technique. Simplifying and segmenting an input language text becomes mandatory in order to improve the machine translation quality. The experimental results show that the proposed approach competes with the machine translation methods reported in the literature and it provides the best translated output in each language.