• 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.

• 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.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.165-171
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• 2021

The current study investigated cyber learners' use and perceptions of online machine translation (MT) tools. The results show that learners use several MT tools frequently and extensively for various second language learning (L2) purposes according to their needs. The learners' overall perceptions of using MT for English learning were generally positive. The learners reported several advantages of machine translation: ease of use, helpful feedback, effective revision, and facilitation of self-directed learning. At the same time, a considerable number of learners were aware of MT's drawbacks, such as awkward sentences, inaccurate grammar, and inappropriate words, and thus held a negative or skeptical view on the quality and accuracy of MT. These findings have important pedagogical implications for using MT in the context of a cyber university. For successful integration of MT in English classes, teachers need to provide appropriate guidelines and training that will help learners use MT effectively.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.105-114
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• 2007

This paper addresses a method for customizing an English-to-Korean machine translation system from general domain to patent domain. The customizing method consists of following steps: 1) linguistically studying about characteristics of patent documents, 2) extracting unknown words from large patent documents and constructing large bilingual terminology, 3) extracting and constructing the patent-specific translation patterns 4) customizing the translation engine modules of the existing general MT system according to linguistic study about characteristics of patent documents, and 5) evaluating the accuracy of translation modules and the translation quality. This research was performed under the auspices of the MIC (Ministry of Information and Communication) of Korean government during 2005-2006. The translation accuracy of the customized English-Korean patent translation system is 82.43% on the average in 5 patent fields (machinery, electronics, chemistry, medicine and computer) according to the evaluation of 7 professional human translators. In 2006, the patent MT system started an on-line patent MT service in IPAC (International Patent Assistance Center) under MOCIE (Ministry of Commerce, Industry and Energy) in Korea. In 2007, KIPO (Korean Intellectual Property Office) tries to launch an English-Korean patent MT service.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.245-253
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• 2022

Commenced in 1954 by IBM, machine translation has expanded immensely, particularly in this period. Machine translation can be broken into seven main steps namely- token generation, analyzing morphology, lexeme, tagging Part of Speech, chunking, parsing, and disambiguation in words. Morphological analysis plays a major role when translating Indian languages to develop accurate parts of speech taggers and word sense. The paper presents various machine translation methods used by different researchers for Indian languages along with their performance and drawbacks. Further, the paper concentrates on parts of speech (POS) tagging in Marathi dialect using various methods such as rule-based tagging, unigram, bigram, and more. After careful study, it is concluded that for machine translation, parts of speech tagging is a major step. Also, for the Marathi language, the Hidden Markov Model gives the best results for parts of speech tagging with an accuracy of 93% which can be further improved according to the dataset.

• Language and Information
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• v.8 no.1
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• pp.101-116
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• 2004

This paper proposes a statistical model for the translation of prepositions in English-Korean machine translation. In the proposed model, statistical information acquired from unlabeled Korean corpora is used to choose the best translation from several possible translations. Such information includes functional word-verb co-occurrence information, functional word-verb distance information, and noun-postposition co-occurrence information. The model was evaluated with 443 sentences, each of which has a prepositional phrase, and we attained 71.3% accuracy.

• 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.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.221-232
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• 2021

One bottleneck of Bengali to Hindi transfer based machine translation system is the translation of suffixes of noun. The appropriate translation of a nominal suffix often depends on the semantic role of the corresponding noun chunk in the sentence. With the availability of a high performance Bengali morphological analyzer and a basic Bengali parser it is possible to identify the role of each noun chunk. This information may be used for building rules for translating the ambiguous nominal suffixes. As there are some similarities between the uses of Bengali and Hindi nominal suffixes we find that the rules may be identified by linguistically analyzing corpus data. In this paper, we identify rules for the ambiguous four Bengali nominal suffixes from corpus data and evaluate their performances. This set of rules is able to resolve a majority of the nominal suffix ambiguities in Bengali to Hindi transfer based machine translation system. Using the rules, we are able to translate 98.17% Bengali nouns correctly which is much better than the baseline ILMT system's accuracy of 62.8%.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.109-114
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• 2000

A new method for the determination of camera system rotation and translation from in 3-D space using recursive least square method is presented in this paper. With this method, the calculation of the equation is found by a linear algorithm. Where the equation are either given or be obtained by solving five or more point correspondences. Good results can be obtained in the presence if more than the eight point. A main advantage of this new method is that it decouple rotation and translation, and then reduces computation. With respect to error in the solution point number in the input image data, adding one more feature correspondence to required minimum number improves the solution accuracy drastically. However, further increase in the number of feature correspondence improve the solution accuracy only slowly. The algorithm proposed by this paper is used to make camera system rotation and translation easy to recognize even when camera system attached at end effecter of six degrees of freedom industrial robot manipulator are applied industrial field.