scholarly journals Machine Translation from Natural Language to Code Using Long-Short Term Memory

2019 ◽  
pp. 56-63 ◽  
Author(s):  
K. M. Tahsin Hassan Rahit ◽  
Rashidul Hasan Nabil ◽  
Md Hasibul Huq
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals The use of machine translation algorithm based on residual and LSTM neural network in translation teaching

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0240663
Author(s):  
Beibei Ren
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Translation Model ◽  
Translation Quality ◽  
Long Short Term Memory ◽  
Deep Learning Neural Network

With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model’s performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Ralph Sherwin A. Corpuz ◽  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Natural Language ◽  
Text Categorization ◽  
Short Term Memory ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

Analyzing Natural Language Essay Generator Models Using Long Short-Term Memory Neural Networks

2021 ◽  
pp. 233-248
Author(s):  
Mayank Gaur ◽  
Mridul Arora ◽  
Varun Prakash ◽  
Yash Kumar ◽  
Kirti Gupta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals COVID-19 ChatBot

2021 ◽  
Vol 9 (11) ◽  
pp. 44-49
Author(s):  
Satish Tirumalapudi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Control ◽  
Prediction Problems

Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and responses. Long Short-Term Memory (LSTM) is a Recurrent Neural Network that is capable of learning order dependence in sequence prediction problems. One of the most popular RNN approaches is LSTM to identify and control a dynamic system. We use an RNN to classify the category user’s message belongs to and then will give a response from the list of responses. Keywords: NLP – Natural Language Processing, LSTM – Long Short Term Memory, RNN – Recurrent Neural Networks.

scholarly journals Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia

2021 ◽  
Vol 10 (4) ◽  
pp. 354-361
Author(s):  
Yudi Widhiyasana ◽  
Transmissia Semiawan ◽  
Ilham Gibran Achmad Mudzakir ◽  
Muhammad Randi Noor
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Rate ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

scholarly journals Explicit Knowledge-based Reasoning for Visual Question Answering

2017 ◽  
Author(s):  
Peng Wang ◽  
Qi Wu ◽  
Chunhua Shen ◽  
Anthony Dick ◽  
Anton van den Hengel
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Question Answering ◽  
Explicit Knowledge ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Visual Question Answering ◽  
Knowledge Based ◽  
Long Short Term Memory

We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.

Comparative Analysis of Language Translation and Detection System Using Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1200-1211
Author(s):  
Aishwarya R. Verma
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Short Term Memory ◽  
Detection System ◽  
English Text ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract: Words are the meaty component which can be expressed through speech, writing or signals. It is important that the actual message or meaning of the words sent must conveys the same meaning to the one receives. The evolution from manual language translator to the digital machine translation have helped us a lot for finding the exact meaning such that each word must give at least close to exact actual meaning. To make machine translator more human-friendly feeling, natural language processing (NLP) with machine learning (ML) can make the best combination. The main challenges in machine translated sentence can involve ambiguities, lexical divergence, syntactic, lexical mismatches, semantic issues, etc. which can be seen in grammar, spellings, punctuations, spaces, etc. After analysis on different algorithms, we have implemented a two different machine translator using two different Long Short-Term Memory (LSTM) approaches and performed the comparative study of the quality of the translated text based on their respective accuracy. We have used two different training approaches of encodingdecoding techniques using same datasets, which translates the source English text to the target Hindi text. To detect the text entered is English or Hindi language, we have used Sequential LSTM training model for which the analysis has been performed based on its accuracy. As the result, the first LSTM trained model is 84% accurate and the second LSTM trained model is 71% accurate in its translation from English to Hindi text, while the detection LSTM trained model is 78% accurate in detecting English text and 81% accurate in detecting Hindi text. This study has helped us to analyze the appropriate machine translation based on its accuracy. Keywords: Accuracy, Decoding, Machine Learning (ML), Detection System, Encoding, Long Short-Term Memory (LSTM), Machine Translation, Natural Language Processing (NLP), Sequential

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