RETRACTED: Research on English translation long text filtering based on LSTM semantic relevance

Chao, Zheng

doi:10.1016/j.micpro.2020.103574

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…Translation activities require a full understanding of the translation object, a thorough understanding of the corpus, and the ability to accurately reproduce the original text in another language according to the standards of faithfulness, elegance, and quality [3,4]. e current translation teaching cannot really meet the requirement of improving students' translation ability.…”

Section: Introductionmentioning

confidence: 99%

Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

2022

Mobile Information Systems

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Translation is an important index to assess students’ English learning ability. In the current English translation teaching, there are still dilemmas such as poor professionalism of curriculum, insufficient knowledge reserve of students, and single teaching methods and approaches. Therefore, this study carries out research on English translation teaching based on the deep learning model to improve the EM algorithm and the guidance of Production-Oriented Approach (POA). The experiment proves that POA can effectively mobilize students’ enthusiasm in translation learning, improve the efficiency of internalizing human knowledge into output ability, optimize teachers’ teaching effect, and significantly improve students' English translation ability and output application ability by more than 30%.

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Section: Introductionmentioning

confidence: 99%

Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

2022

Mobile Information Systems

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“…e main problems with traditional classi cation methods are as follows: the text representation having a high latitude and a high sparseness, weak feature expression ability, requirement of manual feature engineering, and high cost. Deep learning automatically obtains features by using CNN [5,6], RNN [7,8], LSTM [9,10], and other network structures, subtracts complicated arti cial feature engineering, and realizes an end-to-end solution. However, due to the strong dependence of deep learning on manually labeled data, it brings problems, such as insu cient training data and a weak interpretability of the model.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning for Question Classification Based on Semantic Information Features of Category Labels

Kang

et al. 2022

Computational Intelligence and Neuroscience

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Question classification is an important component of the question answering system (QA system), which is designed to restrict the answer types and accurately locate the answers. Therefore, the classification results of the questions affect the quality and performance of the QA system. Most question classification methods in the past have relied on a large amount of manually labeled training data. However, in real situations, especially in new domains, it is very difficult to obtain a large amount of labeled data. Transfer learning is an effective approach to solve the problem with the scarcity of annotated data in new domains. We compare the effects of different deep transfer learning methods on cross-domain question classification. On the basis of the ALBERT fine-tuning model, we extract the category labels of the source domain, the question text, and the predicted category labels of the target domain as input to extract the category labels. Additionally, the semantic information of the category labels is extracted to achieve cross-domain question classification. Furthermore, WordNet is used to expand the question, which further improves the classification accuracy of the target domain. Experimental results show that the above methods can further improve the classification accuracy in new domains based on deep transfer learning.

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Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval Using Deep Convolution Neural Networks

Deshmukh,

Ambhore

2024

Algorithms for Intelligent Systems

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RETRACTED: Research on English translation long text filtering based on LSTM semantic relevance

Cited by 8 publications

References 4 publications

Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

Application of Production-Oriented Approach and Deep Learning Model in English Translation Teaching Model

Deep Transfer Learning for Question Classification Based on Semantic Information Features of Category Labels

Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval Using Deep Convolution Neural Networks

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