• 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.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.1-8
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• 2020

Machine translation refers to a system where a computer translates a source sentence into a target sentence. There are various subfields of machine translation. APE (Automatic Post Editing) is a subfield of machine translation that produces better translations by editing the output of machine translation systems. In other words, it means the process of correcting errors included in the translations generated by the machine translation system to make proofreading. Rather than changing the machine translation model, this is a research field to improve the translation quality by correcting the result sentence of the machine translation system. Since 2015, APE has been selected for the WMT Shaed Task. and the performance evaluation uses TER (Translation Error Rate). Due to this, various studies on the APE model have been published recently, and this paper deals with the latest research trends in the field of APE.

• 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.

• Journal of Genetic Medicine
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• v.10 no.1
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• pp.27-32
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• 2013

Post-transcriptional nucleotide sequence modification of transcripts by RNA editing is an important molecular mechanism in the regulation of protein function and is associated with a variety of human disease phenotypes. Identification of RNA editing sites is the basic step for studying RNA editing. Databases and bioinformatics resources are used to annotate and evaluate as well as identify RNA editing sites. No method is free of limitations. Correctly establishing an analytic pipeline and strategic application of both experimental and bioinformatics methods constitute the first step in investigating RNA editing. This review summarizes modern bioinformatics approaches and related resources for RNA editing research.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.27-35
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• 2021

Automatic post editing is a research field that aims to automatically correct errors in machine translation results. This research is mainly being focus on high resource language pairs, such as English-German. Recent APE studies are mainly adopting transfer learning based research, where pre-training language models, or translation models generated through self-supervised learning methodologies are utilized. While translation based APE model shows superior performance in recent researches, as such researches are conducted on the high resource languages, the same perspective cannot be directly applied to the low resource languages. In this work, we apply two transfer learning strategies to Korean-English APE studies and show that transfer learning with translation model can significantly improves APE performance.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.175-192
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• 2022

Artificial intelligence-based natural language processing technology is playing an important role in helping users write English-language documents. For academic documents in particular, the English proofreading services should reflect the academic characteristics using formal style and technical terms. But the services usually does not because they are based on general English sentences. In addition, since existing studies are mainly for improving the grammatical completeness, there is a limit of fluency improvement. This study proposes an automatic academic English editing methodology to deliver the clear meaning of sentences based on the use of technical terms. The proposed methodology consists of two phases: misspell correction and fluency improvement. In the first phase, appropriate corrective words are provided according to the input typo and contexts. In the second phase, the fluency of the sentence is improved based on the automatic post-editing model of the bidirectional recurrent neural network that can learn from the pair of the original sentence and the edited sentence. Experiments were performed with actual English editing data, and the superiority of the proposed methodology was verified.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.199-208
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• 2021

Automatic Post Editing(APE) is the study that automatically correcting errors included in the machine translated sentences. The goal of APE task is to generate error correcting models that improve translation quality, regardless of the translation system. For training these models, source sentence, machine translation, and post edit, which is manually edited by human translator, are utilized. Especially in the recent APE research, multilingual pretrained language models are being adopted, prior to the training by APE data. This study deals with multilingual pretrained language models adopted to the latest APE researches, and the specific application method for each APE study. Furthermore, based on the current research trend, we propose future research directions utilizing translation model or mBART model.

• Survey Research
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• v.6 no.1
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• pp.83-98
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• 2005

Large scale survey database may contain some erroneous data or missing data. Incomplete or erroneous data may be produced in the process of data collection or data capture. Since erroneous data can cause some bias and inconsistency, data editing, which is the procedure for detecting and adjusting individual errors in data records, is a very important work in statistical survey. In this paper, we introduce an editing process for the housing price survey to enhance discussions on that topic. We explain how to decide some appropriate edit rules and show some related data. Furthermore, we describe input editing procedures which is appropriate for on-line survey and how to find and eliminate erroneous data through output editing.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.44 no.3
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• pp.308-317
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• 2011

A MBES (multibeam echo sounder) survey around Yokji Island, Korea, was conducted to find an effective method for removing error data. Two post-processing software programs, PDS2000 (RESON) and HIPS (CARIS), were used to remove the error data using an interactive editing method and the CUBE algorithm filter. The post-processing with the PDS2000 and HIPS programs, using the interactive editing method, took 120 and 168 hours, respectively, and there was little difference in the seafloor images. The processing time of the PDS2000 and HIPS programs using the CUBE algorithm filter was 36 and 60 hours, respectively. Nevertheless, there was little difference in the seafloor images because of differences in the factor parameters in each of the post-processing programs. Therefore, post-processing using CUBE filtering can save time in data processing and provide consistent results, excluding the subjective decisions of the operator. This method is more effective than other methods for rejecting erroneous multibeam echo sounder data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1074-1083
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• 1993

NC simulation has been used to replace the test cutting of NC machining. Although it can reduce the NC part programming effort, it still has a problem. If any error is found during the simulation, then the part has to be reprogrammed and it is time consumming. This paper presents a method for verifying and editing the NC code after the post-processing without going back to the part programming stage. A data structure and an algorithm to verify and edit the NC code interactively with the aid of graphics is introduced. Z-map method is used for the shaded image display and cross-sectional view display of the macined parts. The method was implemented in a IBM/PC-386 with MS-Windows software, and the multi-window function of the of the MS-Windows is used for the simultaneous editing and verification.