Translation Rules and ANN based model for English to Urdu Machine Translation

Abstract

In this paper we discuss the working of our English to Urdu Machine Translation (MT) system. We used feed-forward back-propagation artificial neural network for the selection of Urdu words/tokens (such as verb, noun/pronoun etc.) and translation rules for grammar structure equivalent to English words/tokens and grammar structure rules respectively. As English is SVO class language while Urdu is SOV class language so grammar structure transfer is main task in English-Urdu machine translation problem. Our system is able to translate sentences having gerund, having infinitives (maximum two), having prepositions and prepositional objects (maximum three), direct object, indirect object etc. Neural network works as the knowledge base for linguistic rules and bilingual dictionary. Bilingual dictionary not only stores the meaning of English word in Urdu but also stores linguistic features attached to the word. The output of our system is presented in Romanized Urdu. The n-gram blue score achieved by the system is 0.6954; METEOR score achieved is 0.8583 and F-score of 0.8650.