A lightweight mobile RSVP for unicast

Chen, Wen; Lei, Feiyu; Chen, Xi; Wang, Weinong

doi:10.1007/bf02832424

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…The approach has aided in modeling continuous representations of linguistics. It has also shown promising results when applied to other domains of research such as computer vision (Krizhevsky, 2012) [76], speech recognition (Hinton et al) [77], character recognition (Ling et al, 2015) [78] etc. Its tremendous rise in performance and overall technological advances have made it state-of-the-art.…”

Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%

Machine Translation Systems Based on Classical-Statistical-Deep-Learning Approaches

et al. 2023

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Over recent years, machine translation has achieved astounding accomplishments. Machine translation has become more evident with the need to understand the information available on the internet in different languages and due to the up-scaled exchange in international trade. The enhanced computing speed due to advancements in the hardware components and easy accessibility of the monolingual and bilingual data are the significant factors that have added up to boost the success of machine translation. This paper investigates the machine translation models developed so far to the current state-of-the-art providing a solid understanding of different architectures with the comparative evaluation and future directions for the translation task. Because hybrid models, neural machine translation, and statistical machine translation are the types of machine translation that are utilized the most frequently, it is essential to have an understanding of how each one functions. A comprehensive comprehension of the several approaches to machine translation would be made possible as a result of this. In order to understand the advantages and disadvantages of the various approaches, it is necessary to conduct an in-depth comparison of several models on a variety of benchmark datasets. The accuracy of translations from multiple models is compared using metrics such as the BLEU score, TER score, and METEOR score.

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