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DBpia AI
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AI 아이디어
AI 리더
AI 뷰어
Citeasy
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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
이용수
초록· 키워드
Post-editing refers to correcting and editing machine translation results, which means its fundamentals involve revision and quality assessment. Assessing machine translation quality is a subjective process that relies on human judgment, which is inferred from complex criteria and parameters in professional quality metrics. Thus, a growing motivation started to be placed on the creation of machine translation error typology based upon theoretical reviews. This paper examines theoretical approaches on criteria of translation review, quality assessment and MT post-editing, and then discusses machine translation errors based on the criteria of form and meaning (language use). The paper also analyzes error types with high frequency in machine translation and compares them with those in human translation. The major error types observed in machine translation are accuracy (language norm) and clarity (transfer of meaning), while stylistic errors such as consistency and redundancy affects readability in human translation. In particular, morphological errors in orthography and syntactic restructuring have a negative effect on machine translation results, which is presumably incurred by the way how MT processes and retrieves language data.
#Revisiting Machine Translation Error Typology through Human Post-editing [machine translation
#post-editing
#revision
#evaluation
#Translation Quality Assessment(TQA)]
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