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Text-Text Neural Machine Translation: A Survey

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Abstract

We present a review of Neural Machine Translation (NMT), which has got much popularity in recent decades. Machine translation eased the way we do massive language translation in the new digital era. Otherwise, language translation would have been manually done by human experts. However, manual translation is very costly, time-consuming, and prominently inefficient. So far, three main Machine Translation (MT) techniques have been developed over the past few decades. Viz rule-based, statistical, and neural machine translations. We have presented the merits and demerits of each of these methods and discussed a more detailed review of articles under each category. In the present survey, we conducted an in-depth review of existing approaches, basic architecture, and models for MT systems. Our effort is to shed light on the existing MT systems and assist potential researchers, in revealing related works in the literature. In the process, critical research gaps have been identified. This review intrinsically helps researchers who are interested in the study of MT.

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  1. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, 600062, Chennai, India

    Ebisa Gemechu & G. R. Kanagachidambaresan

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  1. Ebisa Gemechu
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  2. G. R. Kanagachidambaresan
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Correspondence to Ebisa Gemechu or G. R. Kanagachidambaresan.

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Ebisa Gemechu, Kanagachidambaresan, G.R. Text-Text Neural Machine Translation: A Survey. Opt. Mem. Neural Networks 32, 59–72 (2023). https://doi.org/10.3103/S1060992X23020042

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