유의어 한일 번역을 통해 알아본 ‘인공신경망 기계번역’의 의미 관계성 인지 능력 연구 (A Study on the Cognitive Ability of ‘Neural Network Machine Translations’ Relating to Semantic Relationship Based on Korean-Japanese Translations of Synonyms)

서지정보

· 발행기관 : 일본어문학회
· 수록지 정보 : 일본어문학 / 100호 / 153 ~ 174페이지
· 저자명 : 장혜선

초록

本研究は、「類義語」の翻訳過程で「単語が使われている状況と文脈、そして類義語の微妙な違いを機械翻訳システムが正確に認知し、類義語の中から最も適切な語彙を選択できるか」という疑問から始まった。類義語の辞書的意味は「語形は異なっているが、意味の似かよっている二つ以上の語」だが、たとえ意味がほとんど一致していたり類似していても、それぞれの語彙は使われる場面や文脈によって明確に使い分けなければならない。人間翻訳が行われる際、専門の翻訳者には文脈と状況に相応しい最も適切な単語を区別して選別できる能力がある。ニューラル機械翻訳の出現によって翻訳エラーが改善し、翻訳の品質が著しく向上したとはいえ、果たして機械が語彙が使われている状況と文脈まで正確に認知できるのか、その能力を検証することが本稿の目標である。本稿では、ニューラル機械翻訳プログラムであるNaverのパパゴを使用して、代表的な類義語（日本語）6組をそれぞれ韓日、日韓の言語方向に翻訳することで、ニューラル機械翻訳システムが文章内の単語を通じて場面や文脈をある程度認知し、適切な語彙選択へと繋げることができるということを確認することができた。

영어초록

With the spread of "GNUMT (Google Neural Network Machine Translation)" programs, various machine translation errors based on previous "RBMT (Rule-Based Machine Translation)" or "Statistical Machine Translation (SMT)" methods have greatly diminished, leading to high satisfactions on the part of users. Unlike past methods, NMT (Neural Network Machine Translation) employs deep-learning methods. Thus, translation quality hinges on the amount of data in NMT. As translation quality will increase with the accumulation of data, artificial NMT is expected to continue improving at a fairly rapid pace. Compared to other language pairs, the Korean-Japanese pair often produces high quality translations since the two languages have quite similar syntactic structures and Chinese characters. Today, Artificial Intelligence identifies the context of the original text and draws appropriate expressions from accumulated corpus data.
This study stemmed from the question of 'When translating synonyms, can MT (Machine Translation) recognize a situation and context and select appropriate expressions among synonyms?' The dictionary definition of synonyms is 'words with similar meaning', and even if the meanings are almost the same or similar, each word must be clearly distinguished according to the situation or context. When human translation is performed, professional translators are capable of selecting appropriate words suitable for the context and situation. Although translation quality has improved significantly due to the NMT, this paper aims to verify the ability of the machine to accurately recognize the situation and context in which the word is used. In this paper, by using a neural machine translation program, Naver's Papago, to translate six pairs of representative synonyms (Japanese) in both directions of Korean-Japanese and Japanese-Korean, we have confirmed that the neural machine translation system can recognize some degree of scenes and contexts through words in a sentence and lead to appropriate lexical selection.

참고자료

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