An Analysis of Josa and Eomi in Translating Korean TV Dramas Into English With Artificial Intelligence

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).