@article{(Open Science Index):https://publications.waset.org/pdf/10011951,
	  title     = {End-to-End Spanish-English Sequence Learning Translation Model},
	  author    = {Vidhu Mitha Goutham and  Ruma Mukherjee},
	  country	= {},
	  institution	= {},
	  abstract     = {The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {4},
	  year      = {2021},
	  pages     = {248 - 251},
	  ee        = {https://publications.waset.org/pdf/10011951},
	  url   	= {https://publications.waset.org/vol/172},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 172, 2021},
	}