Context based machine translation with recurrent neural network for English–Amharic translation

Ashengo, Yeabsira Asefa; Aga, Rosa Tsegaye; Abebe, Surafel Lemma

doi:10.1007/s10590-021-09262-4

Cited by 19 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where y i is the irregular logarithmic probability of the output word I, which is calculated by the formula composed of parameters b, W, U, d, and H, as shown in the following formula (13):…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google’s translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To transform the problem of natural language processing into a problem of machine learning, the first step must be to find a way to mathematicize these symbols [ 12 , 13 ]. In natural language processing, the simplest word representation is one-hot representation.…”

Section: Methodsmentioning

confidence: 99%

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In this process, the source information x is the core of the translation model and gives the source semantics required to generate the target word y j , so that the generated translation can faithfully reflect the meaning of the source sentence; part of the target translation information y z is the core of the language model. It gives the sentence context of the target word y j , which can help the generated translation to be fluent and natural [32]. Figure 2 shows the overall structure of English translation.…”

Section: Contextual Feature Extractionmentioning

confidence: 99%

English Translation Model Based on Intelligent Recognition and Deep Learning

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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.

show abstract

“…However, the focus of this translation method is to divide sentences into words, ignoring the influence of the positional relationship between words. With the development of statistical machine translation, it has gradually become the mainstream of machine translation, but there are still some problems to be solved in practical application, such as linear indivisibility, difficult design features, and insufficient utilization of nonlocal context [ 13 ]. By 2012, deep learning came into people's vision and the stage of explosive development began.…”

Section: Development and Current Situation Of Machine Translation Res...mentioning

confidence: 99%

English Lexical Analysis System of Machine Translation Based on Simple Recurrent Neural Network

Zhu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the increasing communication between countries, machine translation has become an important way of communication between ethnic groups in different language systems. In order to further improve the coverage mechanism and vocabulary translation quality of the machine translation model of neural network, this paper will study the machine translation English lexical analysis system based on simple recurrent neural network. In order to improve the effect of word alignment, marking and attention mechanism are introduced into the English lexical analysis model. The experimental results show that the tagging of named entity words in the system can reduce the problem of unregistered words. Combined with the attention mechanism, it can significantly improve the effect of word alignment and label recall. It not only improves the controllability of translation but also has a positive impact on the quality of translation and its application effect in specific scenes. The evaluation of translation quality indicators shows that the system can effectively improve the accuracy and quality of Chinese-English translation.

show abstract

Context based machine translation with recurrent neural network for English–Amharic translation

Cited by 19 publications

References 13 publications

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

English Translation Model Based on Intelligent Recognition and Deep Learning

English Lexical Analysis System of Machine Translation Based on Simple Recurrent Neural Network

Contact Info

Product

Resources

About