Design and Development of Intelligent Translation System for French Translation Process using Gated Neural Network

In the recent times, the most of the resources are mostly available in English, multi-lingual translation is urgently required for the penetration of essence of the education to the deep roots of society. Neural machine translation (NMT) is one such intelligent technique which usually deployed for an efficient translation process from one source of language to another language. But these NMT techniques substantially requires the large corpus of data to achieve the improved translation process with reduced errors in the translation process. To overcome this aforementioned challenge, this research article ensembles the automatic error detection systems with an enhanced framework of Encoder-Decoder architecture which consist of an enhanced version of GRU-Gated recurrent neural network architecture with the multi-headed attention networks. The designed architecture extracts word patterns from a parallel corpus during training, forming a French vocabulary via Kaggle, and its effectiveness is evaluated using measures like Bilingual Evaluation Understudy (BLEU), character-level F-score (chrF) and Word Error Rate (WER). To prove the excellence of the proposed model, extensive comparison between the proposed and existing architectures is compared and its performance metrics are analysed. Outcomes depict that the proposed architecture has shown the improvised NMT by achieving the BLEU as 39.2 and low WER when compared to the exisiting models. These experimental results promise the strong hold for further experimentation with the French based NMT process.