End-to-end spanish-english sequence learning translation model

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Vidhu Mitha Goutham

Unisys Corporation, India

: J Comput Eng Inf Technol

Abstract

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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 open source models are scarce. We survey and compare current translation techniques and propose to you a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoderdecoder model is now paving the path for higher precision levels in translation. Using a CNN encoder and an 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 duolingo 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.

Biography

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Vidhu Mitha Goutham is an IT Engineer working at Unisys India as a Data Analyst and is working on pursuing a Masters at Degree in the USA. She is a skilled leader and currently heads a team of 850+ student members at one of her many founding ventures which also happens to be of the largest Rotaract Clubs in the world.