Synthetic data with neural machine translation for automatic correction in arabic grammar

Solyman, Aiman; Wang, Zhenyu; Qian, Tao; Elhag, Arafat Abdulgader Mohammed; Toseef, Muhammad; Aleibeid, Zeinab

doi:10.1016/j.eij.2020.12.001

Cited by 29 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional machine translation method is the SMT model, which counts information such as word pairs, parallel phrase pairs, and parallel syntactic structures from large-scale parallel corpus to establish a statistical model for the translation process. The research methods mainly include word-based statistical methods [12], phrase-based statistical methods [13], and syntactic structure-based statistical methods [14]. Among them, the phrase-based statistical machine translation takes phrases (that is, any consecutive words) as the basic translation unit, which can well solve the dependency relationship between the local contexts of sentences, and the translation quality has been greatly improved compared with the word-based statistical method.…”

Section: Introductionmentioning

confidence: 99%

English-Chinese Machine Translation Model Based on Bidirectional Neural Network with Attention Mechanism

Wenjia

2022

Journal of Sensors

View full text Add to dashboard Cite

In recent years, with the development of deep learning, machine translation using neural network has gradually become the mainstream method in industry and academia. The existing Chinese-English machine translation models generally adopt the deep neural network architecture based on attention mechanism. However, it is still a challenging problem to model short and long sequences simultaneously. Therefore, a bidirectional LSTM model integrating attention mechanism is proposed. Firstly, by using the word vector as the input data of the translation model, the linguistic symbols used in the translation process are mathematized. Secondly, two attention mechanisms are designed: local attention mechanism and global attention mechanism. The local attention mechanism is mainly used to learn which words or phrases in the input sequence are more important for modeling, while the global attention mechanism is used to learn which layer of expression vector in the input sequence is more critical. Bidirectional LSTM can better fuse the feature information in the input sequence, while bidirectional LSTM with attention mechanism can simultaneously model short and long sequences. The experimental results show that compared with many existing translation models, the bidirectional LSTM model with attention mechanism can effectively improve the quality of machine translation.

show abstract

Section: Introductionmentioning

confidence: 99%

English-Chinese Machine Translation Model Based on Bidirectional Neural Network with Attention Mechanism

Wenjia

2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…In the Decoder phase, the model still uses GRU units. The hidden layer state in the decoding network is called d = { d 1 , d 2 ,…, d Ty }, and the probability distribution information of the current output sequence y 1 , y 2 ,…, y Ty is as follows [ 25 ]:

g is a function of the nonlinear multi-layer structure. The user calculates the probability distribution information of output y i .…”

Section: Methodsmentioning

confidence: 99%

A Computational Neural Network Model for College English Grammar Correction

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

For the error correction of English grammar, if there are errors in the semantic units (words and sentences), it will inevitably affect the subsequent text analysis and semantic understanding, and ultimately reduce the overall performance of the practical application system. Therefore, intelligent error detection and correction of the word and grammatical errors in English texts is one of the key and difficult points of natural language processing. This exploration innovatively combines a computational neural model with college grammar error correction to improve the accuracy of college grammar error correction. It studies the computational neural model in English grammar error correction based on a neural network named Knowledge and Neural machine translation powered College English Grammar Typo Correction (KNGTC). First, the Recurrent Neural Network is introduced, and the overall structure of the English grammatical error correction neural model is constructed. Moreover, the supervised training of Attention is discussed, and the experimental environment and experimental data are given. The results show that KNGTC has high accuracy in college English grammar correction, and the accuracy of this model in CET-4 and CET-6 writing can reach 82.69%. The English grammar error correction model based on the computational neural network has perfect function and strong error correction ability. The optimization and perfection of the model can improve students’ English grammar level, which has certain practical value. After years of continuous optimization and improvement, English grammar error correction technology has entered a performance bottleneck. This mode’s construction can break the current technology’s limitations and bring a better user experience. Therefore, it is very valuable to study the error correction model of English grammar in practical application.

show abstract

“…Solyman et al [73] proposed an unsupervised method to generate large-scale synthetic training data to overcome the challenge of the scarcity of training data for Arabic. The method is based on confusion function to increase the amount of training set.…”

Section: ) Error Analysismentioning

confidence: 99%

“…Some research investigated MT samples with native speakers so they could review the linguistic aspects of MT errors [13], [40]; other research works used neural networks to detect errors [41], [73] or to correct them [42].…”

Section: ) Error Analysismentioning

confidence: 99%