Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Ji, Honggeun; Oh, Soyoung; Kim, Jina; Choi, Seung Wook; Park, Eunil

doi:10.32604/cmc.2022.019521

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…The measurement of the performance of each classification technique was conducted independently, and all findings were documented for a deeper examination. These measures were applied to evaluate the quality of the trained classifiers [68][69][70]. A critical metric, the confusion matrix, reflects four expected values: (TP) true positives, which are events in which the prediction is yes, and passengers are satisfied.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers' expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers' pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deeplearning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

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Section: Evaluation and Resultsmentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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“…To classify the labelled headlines, we experimented with Bi-LSTM and CNN. Since more than 70% of the annotated headlines and thumbnails in the dataset were agro , we employed SMOTE to address the data imbalance issue [61]. Then, we randomly divided the collected headlines into training (1354, 72%), validation (150, 8%) and testing (377, 20%) sets.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Detecting agro: Korean trolling and clickbaiting behaviour in online environments

Choi

Kim²,

Jeong

et al. 2022

Journal of Information Science

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This article presents one of the first approaches to provide the understanding of agro (one of the unique eye-attracting cues) headlines and thumbnails in online video sharing platform, YouTube. We annotated 1881 headlines and thumbnails, based on agro and the type of agro. Then, we experimented with machine learning models to classify agro data from the non- agro data. With a bidirectional long short-term memory (Bi-LSTM) model, we achieved 84.35% of accuracy in detecting agro headlines and 82.80% of accuracy in detecting agro thumbnails. We believe that the automatic detection of agro headlines can allow users to have better experience in browsing through and getting the content that they want online.

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“…With continuous breakthroughs in research, the research on machine translation technology has gradually shifted from a translation system (based on vocabulary, grammar and other rules) to a statistical-based machine translation, and then to the current research, the neural machine translation which is on the hot. The task of neural machine translation is mainly to use neural network related methods and a large amount of data for training and get a general translation model [5]. After the model is trained, we only need to input the source language sentence into the given model, and the model can get the corresponding translation result by performing the calculation.…”

Section: Introductionmentioning

confidence: 99%

Neural Machine Translation by Fusing Key Information of Text

Hu¹,

Li²,

Bai³

et al. 2023

Computers, Materials &Amp; Continua

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When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results' quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model's performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.

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Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Cited by 10 publications

References 19 publications

An Optimized Deep Learning Approach for Improving Airline Services

An Optimized Deep Learning Approach for Improving Airline Services

Detecting agro: Korean trolling and clickbaiting behaviour in online environments

Neural Machine Translation by Fusing Key Information of Text

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