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Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model

2019-11-26 05:59:41
Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa
arXiv_CL
     
Abstract

An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-side monolingual data. Recently, iterative back-translation has been shown to outperform standard back-translation albeit on some language pairs. This work proposes the iterative batch back-translation that is aimed at enhancing the standard iterative back-translation and enabling the efficient utilization of more monolingual data. After each iteration, improved back-translations of new sentences are added to the parallel data that will be used to train the final forward model. The work presents a conceptual model of the proposed approach.

Abstract (translated)

URL

https://arxiv.org/abs/2001.11327

PDF

https://arxiv.org/pdf/2001.11327.pdf

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