Enhancing Cross Language for English-Telugu pairs through the Modified Transformer Model based Neural Machine Translation
DOI:
https://doi.org/10.22399/ijcesen.1740Keywords:
Cross Language Translation, Transformer Networks, Neural machine translation, Feed Forward networks, Multi-scale attention mapsAbstract
Cross-Language Translation (CLT) refers to conventional automated systems that generate translations between natural languages without human involvement. As the most of the resources are mostly available in English, multi-lingual translation is badly 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. This bottleneck makes the NMT to apply for the mid-resource language compared to its dominant English counterparts. Although some languages benefit from established NMT systems, creating one for low-resource languages is a challenge due to their intricate morphology and lack of non-parallel data. To overcome this aforementioned problem, this research article proposes the modified transformer architecture for NMT to improve the translation efficiency of the NMT. The proposed NMT framework, consist of Encoder-Decoder architecture which consist of enhanced version of transformer architecture with the multiple fast feed forward networks and multi-headed soft attention networks. The designed architecture extracts word patterns from a parallel corpus during training, forming an English–Telugu 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 0.89 and low WER when compared to the existing models. These experimental results promise the strong hold for further experimentation with the multi-lingual based NMT process.
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