Application of Computer-aided Translation in English Teaching

Yao, Shuping

doi:10.3991/ijet.v12i08.7145

Cited by 43 publications

(37 citation statements)

References 6 publications

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“…Machine Translation (MT) is defined as "translation from one natural language (source language (SL)) to another language (target language (TL)) using computerized systems and, with or without human assistance" [18]. Different applications can benefit in translations [19] [20], thus, NLP researchers devote their time towards creation of a good translator. These were done in application to different language since each language has their own unique structure.…”

Section: Related Workmentioning

confidence: 99%

A Bicolano-to-Tagalog Transfer-Based Machine Translation System

2019

IJRTE

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The Bicolano-Tagalog Transfer-based Machine Translation System is a unidirectional machine translator for languages Bicolano and Tagalog. The transfer-based approach is divided into three phase: Pre-Processing Analysis, Morphological Transfer, and Sentence Generation. The system analyze first the source language (Bicolano) input to create some internal representation. This includes the tokenizer, stemmer, POS tag and parser. Through transfer rules, it then typically manipulates this internal representation to transfer parsed source language syntactic structure into target language syntactic structure. Finally, the system generates Tagalog sentence from own morphological and syntactic information. Each phase will undergo training and evaluation test for the competence of end-results. Overall performance shows a 71.71% accuracy rate.

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Section: Related Workmentioning

confidence: 99%

A Bicolano-to-Tagalog Transfer-Based Machine Translation System

2019

IJRTE

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“…The applications of technologies to translation studies have always been the core concern of translation professionals and software developers because of the increasing demands for translation services [1][2][3]. This is due to the very clear fact that nowadays conventional methods of translation no longer satisfy the needs of translation users all over the world, particularly if we consider the unparalleled availability and easy accessibility to data and information in different languages [2,4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the assumption that there are new demands in the field of translation, and attempting to fulfill this gap, different technological tools have been introduced to the field of translation, with the intention to offer new systems that intended for enhancing the process of translation, both in quality and delivery [3]. Among these new systems are: SYSTRAN, Memsource, SDL Trados Studios, and CSOFT.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Translation Software on Improving the Performance of Translation Majors

Omar¹,

Khafaga²,

Shaalan³

2020

IJACSA

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The recent years have witnessed a high demand for professional translation services due to the global nature of world economy, accessibility to data in different languages, and the development of unprecedented communication channels. Translators can no longer meet these growing needs of customers and businesses. As such, different translation technologies have been developed to help translation learners and professionals improve their performance in producing a high quality translation. These technologies are now widely integrated into translation programs in different universities, institutes and training centers around the world for their usefulness and reliability in improving the performance of translation learners and professionals in the delivery of trustworthy and professional translation services. Nevertheless, surveys show that the Saudi labor market still has serious problems with qualified translators who are familiar with translation technologies that have negative impacts on the quality and delivery of translation services. This study, therefore, seeks to explore the opportunities and challenges of incorporating translation software and latest technologies into translation pedagogy in the Saudi universities. An open-ended interview with 37 translation instructors from 9 Saudi universities was conducted. Results indicate that the integration of translation software and technologies is still less than expected. This can be attributed to the fact that the majority of instructors prefer manual translation over computer-assisted translation (CAT), translation technologies are not provided by the institutions, and learning outcomes are not linked to labor market needs.

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“…It can enhance students' cognition of human translation and machine translation, and cultivate their ability to apply machine translation [10]. Yao (2017) found that more English teachers worldwide began to try to apply machine translation to the teaching process, but few applications in China [11]. A survey by Wu and Zhang (2018) showed that compared with traditional English teaching, students who use machine translation have a better understanding of the classroom and help to improve translation skills [12].…”

Section: Introductionmentioning

confidence: 99%

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

Ren¹

2020

PLoS ONE

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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.

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Application of Computer-aided Translation in English Teaching

Cited by 43 publications

References 6 publications

A Bicolano-to-Tagalog Transfer-Based Machine Translation System

A Bicolano-to-Tagalog Transfer-Based Machine Translation System

The Impact of Translation Software on Improving the Performance of Translation Majors

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

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