Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study

Hermanto, Andi; Adji, Teguh Bharata; Setiawan, Noor Akhmad

doi:10.1109/icsitech.2015.7407791

Cited by 30 publications

(17 citation statements)

References 4 publications

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“…Finally, through practical verification, the model can better evaluate English teaching and improve the effect. Hermanto et al [9] used two models, namely, recurrent neural network (RNN) and statistical network, using the n-gram model. Compared with the statistical language model, the neural language model achieves better results in the field of machine translation.…”

Section: Introductionmentioning

confidence: 99%

Construction of the Open Oral Evaluation Model Based on the Neural Network

Zhi-xin

Zhang

et al. 2021

Scientific Programming

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According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model and text scoring model and makes a specific implementation of two scoring models, respectively. An experimental environment is then built to preprocess the data, and finally, the model built in this study is trained and simulated. The experimental results show that the CNN + LSTM network evaluation model has a better comprehensive scoring performance, higher scoring efficiency, and higher accuracy and has feasibility and practicability.

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Section: Introductionmentioning

confidence: 99%

Construction of the Open Oral Evaluation Model Based on the Neural Network

Zhi-xin

Zhang

et al. 2021

Scientific Programming

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“…We have perceived, in various papers i.e., [ 39 , 95 ] that authors have used the same architecture in the model but obtained different results. Furthermore, in [ 61 , 85 ] researchers add more layers in the network of models such as CNN to enhance the performance of the DL model, which is a key aspect that is overlooked in expert knowledge. The authors obtained efficient performance, implement novel preprocessing, and data augmentation techniques along with deep-learning models.…”

Section: Discussionmentioning

confidence: 99%

“…Some other models that prevent a form of gradient exploding or gradient vanishing have been presented to tackle this problem such as long short-term memory (LSTM) [ 57 , 58 , 59 , 60 ]. The RNN and its variants have attained great performance in various applications such as machine translation, natural language processing, and speech recognition [ 61 , 62 , 63 , 64 , 65 ].…”

Section: Typical Deep-learning Modelsmentioning

confidence: 99%

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

et al. 2020

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Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.

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“…A. Hermanto et.al [22] present Recurrent Neural Network Language approach for English to Indonesian machine translation. A total 10462 training sentence from BPPT used in Bilingual Evaluation Score) RIBES for evaluation.…”

Section: Previous Indonesian Machine Translationmentioning

confidence: 99%

Machine translation of Indonesian: a review

Septarina

Rahutomo

Sarosa

2019

CST

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Nowadays, machine translation has an important role in general communication. The need for machine translation system is higher in this era, resolving culture and nation boundary. Finding appropriate and optimal translation is not an easy task in language processing. Several machine translation systems already exist, but the quality of the translation is needed to be improved further. This paper discusses machine translation researches that involve Indonesian language to the other languages by systematic literature review. This paper exposes different approaches and tools for machine translation. The approaches also use various evaluation methods to measure performance. Moreover, this paper proposes several future works to improve the machine translation quality of Indonesian to other languages. The review results show that the attentionbased approach is being increasingly used to improve the performance of neural machine translation. The translation performance quality depends on the number of the corpus, well-behaved aligned corpus, and the technique used.

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Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study

Cited by 30 publications

References 4 publications

Construction of the Open Oral Evaluation Model Based on the Neural Network

Construction of the Open Oral Evaluation Model Based on the Neural Network

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Machine translation of Indonesian: a review

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