• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.17-22
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• 1999

It is difficult to integrate the speech processing systems and machine translation system in the spoken language translation system by reason that each system uses its own data and basic processing unit. So, we need a common I/O unit which is used in the whole system. In this paper, we propose a Pscudo-Morpheme as the interface between speech processing systems and language translation system. We implement a morphological analysis system for Pseudo-morpheme. The speech processing system using this pseudo-morpheme can get better result than other systems using the phrase or the general morpheme. So, the quality of the whole spoken language translation system can be improved. The analysis-ratio of our implemented system is 98.9%. This is similar to the common morphological analysis systems.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.64-70
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• 2004

Korean and Japanese have same structure of sentences because they belong to same family of languages. So, The transfer driven machine translation is most efficient to translate each other. This paper introduce a method which creates a transfer dictionary for Token Based Transfer Driven Koran-Japanese Machine Translation(TB-TDMT). If the transfer dictionaries are created well, we get rid of useless effort for traditional parsing by performing shallow parsing. The semi-parser makes the dependency tree which has minimum information needed output generating module. We constructed the transfer dictionaries by using the corpus obtained from ETRI spoken language database. Our system was tested with 900 utterances which are collected from travel planning domain. The success-ratio of our system is $92\%$ on restricted testing environment and $81\%$ on unrestricted testing environment.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1076-1081
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• 1994

This paper presents the linguistic processing for the Automatic Interpretation system between English/Korean language pair. We introduce two machine translation systems, each for English-to-Korean and Korean-to-English, describe the system configuration and several characteristics, and discuss the translation evaluation results.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.790-797
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• 2005

It is well-known that there are many similarities between Japanese and Korean language. For example, the order of words and the nature of the grammatical conjugation of both languages are almost the same. Another similarity is the frequent omission of the subject from a sentence. Moreover, both languages have honorific expressions and the identical concept for expressing nouns in terms of Chinese characters. Using these similarities, we have developed a word-to-word translation system which does away with any deep level analysis of syntactic and semantic structures of the two languages. If we use these similarities, the direct translation method is superior to the internal language translation method or transfer-based translation method. Although the MT system based on the direct translation method is more easily developed than the ones based on other methods, it may have a lot of difficulties when it tries to select the appropriate target word from ambiguous source verbs. In this paper, we propose a new algorithm to extract the meaning of substantives and to make use of the order of the extracted meaning. We could select $86.5\%$ appropriate verbs in the sample sentences from IPAL-verb-dictionary. $13.5\%$ indicates the cases in which we could not distinguish the meaning of substantives. We are convinced, however, that the succeeding rate can be increased by getting rid of the meaning of verbs thatare not used so often.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1046-1049
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• 1987

This paper proposes a Korean-Japanese Machine Translation System that processes unregistered words, modifiers and compound sentences. In mophological analysis, the unregistered words are processed by using unregistered word processing algorithm. The modifiers are processed by consulting noun-attributes and grammar rules. The compound sentence processing algorithm recognizes whether the sentence that includes commas is compound sentence or not. This system performs on IBM-PC/AT DOS using Prolog-1.

• Korean Journal of Cognitive Science
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• v.15 no.2
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• pp.33-42
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• 2004

There are many syntactic similarities between Japanese and Korean language. These similarities enable us to build Japanese-Korean translation systems without depending cm sophisticated syntactic analysis and semantic analysis. To further improve translation accuracy, we have been developing a Japanese-Korean translation system using these similarities for several years. However, there still remain some problems with regard to translation of inflected words, processing of multi-translatable words and so on. In this paper, we propose a new method for Japanese-Koran machine translation by using the relationships of two neighboring words. To solve the problems, we investigate the connection rules of auxiliary verb priority. And we design the translation table, which consists of entry tables and connection form tables. for unambiguous words, we can translate a Japanese word to the corresponding Korean word in terms of direct-matching method by consulting the only entry table. Otherwise we have to evaluate the connection value computed from connection form tables and then we can select the most appropriate target word.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.606-610
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• 2009

We propose a method utilizing BLEU(BiLingual Evaluation Understudy), which is widely used in automatic evaluation of machine translations, for an autonomous assessment of a short essay answer. BLEU evaluates translations with an assumption that the translation by a machine is supposed to be more accurate as it is getting to be more similar to the translation by a human. BLEU scores the translation by comparing the n-grams of translations by a machine and humans. Similarly we score students answers by comparing to multiple reference answers with BLEU. In the experiment, we compute correlation coefficient values between scores of our system and human instructors.

• 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.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.53-65
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• 2009

The part-of-speech determination is necessary for resolving the part-of-speech ambiguity in English-Korean machine translation. The part-of-speech ambiguity causes high parsing complexity and makes the accurate translation difficult. In order to solve the problem, the resolution of the part-of-speech ambiguity must be performed after the lexical analysis and before the parsing. This paper proposes the CatAmRes model, which resolves the part-of-speech ambiguity, and compares the performance with that of other part-of-speech tagging methods. CatAmRes model determines the part-of-speech using the probability distribution from Bayesian network training and the statistical information, which are based on the Penn Treebank corpus. The proposed CatAmRes model consists of Calculator and POSDeterminer. Calculator calculates the degree of appropriateness of the partof-speech, and POSDeterminer determines the part-of-speech of the word based on the calculated values. In the experiment, we measure the performance using sentences from WSJ, Brown, IBM corpus.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.7-13
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• 2020

With the recent World Health Organization (WHO) Declaration of Pandemic for Coronavirus Disease-19 (COVID-19), COVID-19 is a global concern and many deaths continue. To overcome this, there is an increasing need for sharing information between countries and countermeasures related to COVID-19. However, due to linguistic boundaries, smooth exchange and sharing of information has not been achieved. In this paper, we propose a Neural Machine Translation (NMT) model specialized for the COVID-19 domain. Centering on English, a Transformer based bidirectional model was produced for French, Spanish, German, Italian, Russian, and Chinese. Based on the BLEU score, the experimental results showed significant high performance in all language pairs compared to the commercialization system.