Open AccessA novel model based on Sequential Adaptive Memory for English–Hindi Translation
Author(s) -
Saini Sandeep,
Sahula Vineet
Publication year - 2021
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12011
Subject(s) - computer science , machine translation , natural language processing , artificial intelligence , example based machine translation , transfer based machine translation , phrase , translation (biology) , synchronous context free grammar , word (group theory) , sequence (biology) , speech recognition , linguistics , biochemistry , chemistry , philosophy , genetics , messenger rna , biology , gene
Machine‐based language translation has been certainly picking up. Still, machines lag behind the cognitive powers of human beings. Neural Machine Translation (NMT) methods require huge datasets and computational power for high‐quality translation. A novel Sequential Adaptive Memory (SAM) cognitive model‐based machine translation system for English to Hindi translation, was proposed. This model is an augmented version of the Cortical Learning Algorithm (CLA). The SAM is based on the architecture of the neocortex region of the brain, where speech and language comprehension and production take place. The proposed model is capable of learning with smaller datasets. This model employs the sequence to sequence learning approach, which provides better quality translation. It enables the creation of word pairs, dictionaries, and rules for translation. The results of the proposed approach are compared with the traditional phrase‐based SMT approach as well as with the state‐of‐the‐art NMT approach. The results are comparable with the results of the conventional approaches. We illustrate that the limitations of the approaches are won over by the proposed SAM approach. It is observed that SAM is capable of exhibiting satisfactory quality translation for low resource languages as well.