Application of Fuzzy Decision Support in Deep Learning Model of English Translation Pattern Classification - Jinlian Liu (International Journal of Computational Intelligence Systems)
Abstract English translation requires an intense knowledge of words, lexical arrangement, and sentence formation. The translation pattern follows either of the above to provide an understandable output. This article assimilates deep learning and fuzzy decision systems to ensure a highly understandable classification of English translations. Integrated translation pattern classification (ITPC) relies on deep learning to classify patterns based on words, lexicons, and sentences. The network is trained for the highest understandable classification output from the translated sentences. The fuzzy decision process is used to validate and extract new possibilities of the translated patterns. The identified patterns (new) increase the chance of translation efficiency of any complex sentence/ words. This process is a single turn of the learning process until the target pattern with the highest efficiency is observed. Based on the number of turns, the training iterations are varied to confine the complexity of pattern classification.