• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.561-568
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• 2022

This study investigated how pre-service English teachers perceive the effects of machine teaching on their English writing competence. 35 Korean students who are majoring in English education participated in this study. The participants used machine translators for one of the required courses related to English composition. A survey and focus group interview were conducted at the end of the course. They were asked to answer to what degree they perceive the effects of machine translators on their writing in terms of lexical, sentential, and discourse levels. Furthermore, their perspectives on the effects of machine translation on English teaching including limitations of machine translators, were interviewed in more detail. The results show that the participants perceive machine translators quite positively in terms of improving their writing competence, but they also point out some critical limitations of machine translators. These findings have some pedagogical implications for English writing course instructors, English teacher educators, and program developers.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.281-288
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• 2020

This study delves into acceptance on neural network machine translation (NMT) such as Google Translate and Papago that uses technical acceptance model. In conclusion, it turned out that perceived usefulness impacts translators' attitude towards NMT. In other words, if translators determine that NMT is related to their work and the quality of the deliverables is guaranteed, they were more positive towards it. Unlike the existing normative approach that translators feel threatened by NMT, empirical results tell us translators perceive NMT as a business tool and such perception was largely influenced by advices of their colleagues and friends and expectations for use.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.86-96
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• 2021

This cross-sectional survey research carried out with the inquisitive agenda on satisfaction of the translation outcomes as performed by human translation and (machine translation) post-editing. The survey group consisted of 166 Korean translators primarily working with the English, Chinese, and Japanese languages. They were asked to rate the satisfactory level with accuracy, fluency, idiomatic expression, and terminology in the Richter's scale of four. The result reveals that human translation is more satisfactory than post-editing with respect to accuracy, but it is uneasy to assert that accuracy is unsatisfactory in post-editing. On the other hand, the Korean translators are less satisfied with fluency, idiomatic expression, and terminology than accuracy. It can be assumed that although human translation is more satisfactory than post-editing, the accuracy of post-editing seems to be more acknowledged than fluency, idiomatic expression, and terminology, which lead the translators to take the accuracy of raw machine-translation products and to go on to improve the fluency, idiomatic expression, and terminology. Nevertheless, Korean translators believe Korean idiomatic expressions cannot be satisfactorily produced in post-editing, while fluency and terminology can be improved in post-editing.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.259-267
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• 2021

Machine translators first appeared in the 1950s and made a leap in translation accuracy by applying the neural translation system in the 2010s. However, it is still having difficulty in translating complex sentences, which made it inconvenient to use machine translators as an English learning tool. Therefore, this study analyzed the usability of a machine translator as an English learning tool through a backtranslation experiment of as phrases. As analysis tools, Google Translator, Naver Papago, and Microsoft Translator, were used since they are representative machine translators using a neural translation system. As a result of the study, findings are: The usability was significantly different according to each as usage when using a machine translator. Accordingly, as usages in sentences were classified into high, ordinary, and low usability. Unlike previous studies, this study has a research contribution in analyzing the machine translator as a direct learning tool and quantifying the usability of the conjunction as.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1161-1167
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• 2018

The development of machine learning has enabled machines to perform delicate tasks that only humans could do, and thus many companies have introduced machine learning based translators. Existing translators have good performances but they have problems in number translation. The translators often mistranslate numbers when the input sentence includes a large number. Furthermore, the output sentence structure completely changes even if only one number in the input sentence changes. In this paper, first, we optimized a neural machine translation model architecture that uses bidirectional RNN, LSTM, and the attention mechanism through data cleansing and changing the dictionary size. Then, we implemented a number-processing algorithm specialized in number translation and applied it to the neural machine translation model to solve the problems above. The paper includes the data cleansing method, an optimal dictionary size and the number-processing algorithm, as well as experiment results for translation performance based on the BLEU score.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.99-108
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• 2020

This study aimed to investigate beginner-level learners' use of translators in L2 writing, and shed light on its validity in writing performance through their responses on the necessity, reliability and limitation of machine translation. 117 university students from beginner-level L2 writing classes participated in the survey. Additionally, 11 of them were interviewed about their answers. The survey and interview data revealed varying viewpoints such as reliability and effects as well as reasons for choosing translator-assisted writing. The vast majority(76.1%) used web-based machine translators for their writing activities, and employed various strategies to help their insufficient L2 skills and to increase their motivation and confidence. On the other hand, they exhibited its detrimental effects including it could lead to plagiarism, and interfere with the learning process unless they post-edited the given translation. However, translators were viewed as a new, efficient, and valid educational tool for effective and successful L2 writing.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.8-13
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• 2005

Interoperability has been the problem of CAD/CAM systems. Starting from 1980's, national and international organizations have addressed the issue through development and release of standards for the exchange of geometric and nongeometric design data. To CAD/CAM vendors, the task of interpreting and implementing these standards falls into their products. This task is a balancing action between users' needs, available development resources, and the technical specifications of standards. This paper explores an area of CAD/CAM systems development, particularly the implementation of the effective exchange files translators'. A new approach is introduced, which proposes to enclose all the translation operations concerning each exchange format to a separate DLL, thus making a 'plug-in.' Then, this plug-in could be used together with the CAD/CAM system or with specialized translation software. This approach allows to create new translators rapidly and to gain the reliable, high-efficiency, and reusable program code. The second part of the paper concerns the possible problems of translators' development. These difficulties often come from the exchange standards' misunderstanding or ambiguity in standards. All examples come from the authors' practice experiences of dealing with CAD/CAM systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.229-236
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• 2011

In this paper we present light weighted English-to-Korean and Korean-to-English mobile translators on smart phones. For natural translation and higher translation quality, translation engines are hybridized with Translation Memory (TM) and Rule-based translation engine. In order to maximize the usability of the system, we combined an Optical Character Recognition (OCR) engine and Text-to-Speech (TTS) engine as a Front-End and Back-end of the mobile translators. With the BLEU and NIST evaluation metrics, the experimental results show our E-K and K-E mobile translation equality reach 72.4% and 77.7% of Google translators, respectively. This shows the quality of our mobile translators almost reaches the that of server-based machine translation to show its commercial usefulness.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.239-244
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• 2022

Neural Machine Translation (NMT) is gradually breaking down the boundary between human and machine translation. We look at actual cases of human and machine translation and discuss why machine translation needs a human touch. In this paper, we raise three driving questions: Can humans be replaced by machines?; How human translators can remain successful in a NMT-driven world?; Is it possible to eliminate language barrier in the era of NMT and World Englishes? The answers to these questions are all negative. We suggest that machine translation is a useful tool with rapidity, accuracy, and low cost productivity. However, the machine translation is limited in the areas of culture, borrowing, ambiguity, new words and (national) dialects. The machines cannot imitate the emotional and intellectual abilities of human translators since machines are based on machine learning, while humans are on intuition. The machine translation will be a useful tool that does not cause moral problems when using methods such as back translation and human post-editing. To conclude, we propose the blended approach that machine translation cannot be completed without the touch of human translation.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.165-171
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• 2018

As the artificial intelligence (AI) plays a crucial role in machine translation (MT) which has loomed large as a new translation paradigm, concerns have also arisen if MT can produce a quality product as human translation (HT) can. In fact, several MT experimental studies report cases in which the MT product called post-editing (PE) as equally as HT or often superior ([1],[2],[6]). As motivated from those studies on translation quality between HT and PE, this study set up an experimental situation in which Korean literature was translated into English, comparatively, by 3 translators and 3 post-editors. Afterwards, a group of 3 other Koreans checked for accuracy of HT and PE; a group of 3 English native speakers scored for fluency of HT and PE. The findings are (1) HT took the translation time, at least, twice longer than PE. (2) Both HT and PE produced similar error types, and Mistranslation and Omission were the major errors for accuracy and Grammar for fluency. (3) HT turned to be inferior to PE for both accuracy and fluency.