Design of machine translator based on QNN

Abstract

Machine translation (MT) along with natural language processing (NLP) always remained an area of interest for researchers since the computers were invented. Many researchers have tried to build the system which can understand multiple languages to translate from one source language to another target language. They also searched the way how computer understand and generate the human languages with semantics and syntactic. However, they realized that still many languages have translation difficulties, grammatically and semantically. Machine translation is a field of natural language processing. It involves the complete linguistic analysis of sentence used for automatic translation from one language to another. The main challenging issues need to be addressed are word ambiguity, word order, word sense, idioms, pronoun resolution, syntactic ambiguity and structural ambiguity. Recently some work has been done with Hindi to English and vice versa by several researchers using different methods of machine translation, like example based system, rule based, statistical machine translation, and parallel machine translation system. Some researchers have described the use of corpus pattern for alignment and reordering of words for English to Hindi machine translation using the neural network, but still there are a lot of possibilities to develop a MT System for Hindi to increase the accuracy of MT. This work presents the machine learning based translation system for Hindi to English and vice versa, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus using the information of parts of speech of individual word in the corpus like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri (Hindi) and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation.