DEVELOPMENT OF AN ANDROID-BASED NLP SYSTEM FOR ENGLISH-TO-IGBO LANGUAGE TRANSLATION

Abstract

Machine Translation (MT) is a process, sometimes referred to as Natural Language Processing which uses a bilingual data set and other language assets to build language and phrase models used to translate text. Statistical Machine Translation utilizes statistical translation models generated from the analysis of monolingual and bilingual training data. Essentially, this approach uses computing power to build sophisticated data models to translate one source language into another. Statistical Machine Translation consists of Language Model (LM), Translation Model (TM) and Decoder. In this research, English to Igbo language translation system has been developed. The Language Model, Translation Model and Decoder is done in Microsoft Hub; training of parallel document, and the language translation system was implemented in Android studio environment, can be accessed through Android application in smartphones. English and Igbo language tokens were determined using Finite State Automata; transition in each state identified the valid token and invalid. Valid tokens were found where transition produces letters, invalid tokens occur when a transition produce combination of digit and letter. English and Igbo language sematic were determined using attribute grammar which was further expressed in parse tree showing the syntax structure. An integrated custom keyboard was developed to input the Igbo words and phrases. Result shows one to one and one to many mapping of English to Igbo words/phrases.