English synchronous real-time translation method based on reinforcement learning

Ke, Xin

doi:10.1007/s11276-022-02910-4

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…These models have been successfully implemented for a variety of tasks in the vision domain such as object detection [91], image super-resolution [92], image segmentation [93], visual question-answering, etc. [94][95][96][97][98].…”

Section: Transformersmentioning

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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Section: Transformersmentioning

confidence: 99%

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

et al. 2023

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“…There are also many occasions where business negotiations might happen between people speaking different languages. Then deepfakes could be used to implement real-time translation between different languages (Ke, 2022) or as an assistive technology translating from or to sign language (Sugandhi, Kumar, and Kaur, 2021; Nguyen et al, 2021;Maghraby et al, 2021), as pointed out years ago by Teich et al (1999).…”

Section: Negotiator Misrepresentationmentioning

confidence: 99%

Videoconference buyer-seller negotiations: a systematic literature review

Prat

2023

Preprint

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Negotiations performed through videoconference have traditionally been studied in comparison to other communication media such as face-to-face, audio channels, or instant messaging. In this systematic literature review, we aim at gathering all empirical studies of buyer-seller negotiations performed synchronously by two people using a video system, analyzing the variables studied and the technological evolution of video negotiations. We develop a framework to classify the relevant variables, dividing them into negotiation, communication, and technological variables, and also in objective or subjective variables. We find that there are mixed or inconclusive results concerning negotiation variables such as economic outcomes, negotiation time, satisfaction, power, or trust. Technological variables like screen size, hardware and software used or user familiarity with technology show the evolution of videoconferencing and are very different among studies, which hinders its comparability. Emerging technologies involving extended reality or artificial intelligence may change the game in the negotiation world.

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A Hybrid Approach for Automatic Text Summarization and Translation based On Luhn, Pegasus, and Textrank Algorithms

Srividya¹,

Bommuluri²,

Asapu³

et al. 2022

2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)

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English synchronous real-time translation method based on reinforcement learning

Cited by 4 publications

References 37 publications

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

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

Videoconference buyer-seller negotiations: a systematic literature review

A Hybrid Approach for Automatic Text Summarization and Translation based On Luhn, Pegasus, and Textrank Algorithms

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