Thinking Like Humans: A New Approach to Machine Translation

Abstract

Existing machine translation approaches do not adequately mimic how humans do translation from one natural language to another. This paper presents a novel approach to machine translation that is inspired by how humans translate natural languages. We have exploited the theory of psycholinguistic sentence-parsing to develop a human-like machine translation system. This approach has been modeled as a multi-agent system, named EnSiMaS, which translates an English sentence into Sinhala sentence. The multi-agent system has been implemented through the MaSMT framework with two manager agents and over 100 agents which deliberate on different aspects of machine translation. These agents are clustered into eight-agent swarms to consider morphological, syntactic, and semantic concerns of the source and the target languages. The EnSiMaS system has been tested with the different types of sentences and successful results were obtained.