English Translation Model Based on Intelligent Recognition and Deep Learning

Abstract

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.

The Impact of Artificial Intelligence on Language Translation: A Review

“…References [1], [3], [11], [30], [31], [51], [32], [39], [55], [72], [73], [78], [81], [85], [88], and [90] specifically examine the assessment of machine translation quality, metrics, and methodologies. References [21], [22], [50], [35], [78], [82], [83], [84], [85], and [91] provide a detailed analysis of machine translation pertaining to particular languages or dialects, including Arabic, Urdu, Sana'ani, and Moroccan Arabic.…”

“…The work conducted by Yu [32] examined many enduring challenges in conventional English translation models, including inadequate precision in English phrase part-of-speech identification, subpar translation quality, and prolonged translation durations. To mitigate these difficulties, the authors propose a novel English translation model that leverages intelligent identification and deeplearning methodologies.…”

English Translation Recognition Model Based on Optimized GLR Algorithm

“…In this paper, the classical GLR algorithm is improved, and the phrase center is proposed to analyze the phrase structure. e improved GLR algorithm realizes the likelihood calculation of the prefixes and postfixes of phrases by means of quaternization, as shown in formula (1).…”

“…With the rapid development of economy, the internet industry is developing rapidly, and the status of English translation in world trade is gradually improving. Machine translation technology can overcome many problems in human translation and reduce the economic consumption and time consumption of human translation [1][2][3]. In the current era of information technology, people's requirements for English translation are gradually increasing, and the need for a computer to understand and translate English language is becoming more urgent [4][5][6][7][8].…”

Construction of English Translation Model Based on Improved Fuzzy Semantic Optimal Control of GLR Algorithm

“…Pharaoh, developed by Philipp Koehn, is the most influential and freely available phrasebased statistical machine translation system available in the public domain. e idea of multi-engine machine translation was proposed, and Pangloss Mark III machine translation system was designed by using the idea of multi-engine machine translation [14]. e system combines rule-based machine translation methods, machine translation method based on the instance, and based on the vocabulary translation machine translation method, the main design idea is as follows: receives input sentences, using the multiple translation engines in parallel translation sentence fragments (phrases and words), will grade each translation unit of storage in a chart, and according to some criteria to grade each translation unit.…”