Quality Estimation of English-Korean Machine Translation using Neural Network based Predictor-Estimator Model

김현,; Shin, Jaehun; Lee, WonKee; 조승우,; Lee, Jong-Hyeok

doi:10.5626/jok.2018.45.6.545

Cited by 2 publications

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“…where θ, c, x, δ represents the symbol on the right side of the action, constraint value, center point symbol, and marking method [21].…”

Section: Corpus Part Of Speech Recognition Based On Intelligentmentioning

confidence: 99%

English Translation Model Based on Intelligent Recognition and Deep Learning

2022

Wireless Communications and Mobile Computing

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Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.

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“…where θ, c, x, δ represents the symbol on the right side of the action, constraint value, center point symbol, and marking method [21].…”

Section: Corpus Part Of Speech Recognition Based On Intelligentmentioning

confidence: 99%

English Translation Model Based on Intelligent Recognition and Deep Learning

2022

Wireless Communications and Mobile Computing

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Word-Level Quality Estimation for Korean-English Neural Machine Translation

Park²,

Moon

et al. 2022

IEEE Access

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With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference sentences. Despite its high utility in the real world, there remain several limitations concerning manual QE data creation: inevitably incurred nontrivial costs due to the need for translation experts, and issues with data scaling and language expansion. To tackle these limitations, we present QUAK, a Korean-English synthetic QE dataset generated in a fully automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and QUAK-H, produced through three strategies that are relatively free from language constraints. Since each strategy requires no human effort, which facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M for QUAK-M. As an experiment, we quantitatively analyze wordlevel QE results in various ways while performing statistical analysis. Moreover, we show that datasets scaled in an efficient way also contribute to performance improvements by observing meaningful performance gains in QUAK-M, P when adding data up to 1.58M. * * Corresponding Author MT outputGiven that the Chinese authorities do not deny it , it is highly likely . pseudo-PEGiven that the Chinese authorities do not deny it , chances are high .

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Quality Estimation of English-Korean Machine Translation using Neural Network based Predictor-Estimator Model

Cited by 2 publications

References 0 publications

English Translation Model Based on Intelligent Recognition and Deep Learning

English Translation Model Based on Intelligent Recognition and Deep Learning

Word-Level Quality Estimation for Korean-English Neural Machine Translation

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