An Analysis of Josa and Eomi in Translating Korean TV Dramas Into English With Artificial Intelligence
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The goal of this study is to find out why the English subtitles of Korean TV dramas have frequent errors. It is anticipated that the findings would shed light on innovative ways for machine translation technology to agglutinate languages. To do this, as a first step, Korean-English subtitles were grammatically tagged according to the category part of speech (POS) to find out which POS has the most frequent errors in each language. Thirty-one groups were analyzed and categorized by tagging the part of speech. Then, for the Korean language, the Kokoma Korean morpheme analyzer was run to tag the Korean script according to the category noun, verb, adjective, etc. These were categorized into forty-five groups. This categorization included nine subsets of josa (postposition) and fourteen of eomi (ending), which are the most difficult parts of Korean to translate into English due to differences in linguistic structure. As a next step, the subtitles were scored and graded as the most corrected and the least corrected by Korean-American bilinguals. The results show that the most frequent error of josa is JX (auxiliary particle) among nine groups whereas the frequent error of eomi is EPT (tense prefinal ending).
Abstract
The goal of this study is to find out why the English subtitles of Korean TV dramas have frequent errors. It is anticipated that the findings would shed light on innovative ways for machine translation technology to agglutinate languages. To do this, as a first step, Korean-English subtitles were grammatically tagged according to the category part of speech (POS) to find out which POS has the most frequent errors in each language. Thirty-one groups were analyzed and categorized by tagging the part of speech. Then, for the Korean language, the Kokoma Korean morpheme analyzer was run to tag the Korean script according to the category noun, verb, adjective, etc. These were categorized into forty-five groups. This categorization included nine subsets of josa (postposition) and fourteen of eomi (ending), which are the most difficult parts of Korean to translate into English due to differences in linguistic structure. As a next step, the subtitles were scored and graded as the most corrected and the least corrected by Korean-American bilinguals. The results show that the most frequent error of josa is JX (auxiliary particle) among nine groups whereas the frequent error of eomi is EPT (tense prefinal ending).
참고문헌 (15)
- Ali, A., "Exploring the problems of machine translation from Arabic into English language faced by Saudi university student of translation at the faculty of arts, Jazan University Saudi Arabia", IOSR Journal of Humanities and Social Science, 21(4), pp.55~66 (2016)[DOI]
- Basari, A., "Subtitles for movie commercial trailers: A technology-based translation", 2017 International Seminar on Application for Technology of Information and Communication (ISemantic), pp.104~108 (2017)
- Can, B., "Turkish POS tagging by reducing sparsity with morpheme tags in small datasets", CICLing 2016 Lecture Notes in Computer Science, pp.320~341 (2016), Springer
- Davis, E. -H., "Machine translation" (2007), Carnegie Mellon University
- Dincer, T., "A suffix based part-of-speech tagger for Turkish", Proceedings of the Fifth International Conference on Information Technology: New Generation, pp.680~685 (2008), IEEE Computer Society
- Gonulal, T., "To err is human: An error analysis approach to Turkish as an L2", The Journal of NCOLCTL, 20(2), pp.107~131 (2016)
- Hong, Y. -K., "A Korean morphological analyzer for speech translation system", Proceeding of Fourth International Conference on Spoken Language Processing, pp.3~6 (1996), ICSLP
- Huh, H. -G., "A resource-based Korean morphological annotation system", Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), pp.37~42 (2005)
- Kim, Y. -B., "Training a Korean SRL system with rich morphological features", Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp.637~642 (2014), Association for Computational Linguistics
- 고성란, "A Comparative Error Analysis of Neural Machine Translation Output: Based on Film Corpus", 언어학, 30(1), pp.157~177 (2022), 대한언어학회[DOI]
- Nguyen, Q. -P., "Korean-Vietnamese neural machine translation system with Korean morphological analysis and word sense disambiguation", IEEE Access, 7, pp.32602~32616 (2019)[DOI]
- "KKAP: KAIST Korean Analysis Platform Morphological Analyzer, POS Tagger, Parser" (2011)[URL]
- 류현진, "Using the Korean Translation of Disney’s Mulan to Learn About Cross-Cultural Awareness", 영상영어교육, 18(3), pp.71~97 (2017), 영상영어교육학회[DOI]
- Shaffer, D, "Erring in English: Korean L1 and cultural interference", KOTESOL Proceedings 2002, pp.219~232 (2015), KOTESOL
- "In Wikipedia"[URL]