機器譯本與人工譯本的差異：基於Coh - Metrix 3.0與詞性標記的定量分析

繆嬋君

doi:10.6342/NTU201602413

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機器譯本與人工譯本的差異：基於Coh - Metrix 3.0與詞性標記的定量分析

Differences between Human and Machine Translations: A Quantitative Analysis Based on Coh-Metrix 3.0 and CLAWS Tagger

繆嬋君 , Masters　　Advisor：高照明　　

英文 DOI： 10.6342/NTU201602413

中譯英翻譯； Coh-Metrix 3.0 ；機器翻譯；人工翻譯；可比語料； CLAWS 詞性標記； Chinese-to-English translation ； Coh-Metrix 3.0 ； machine translation ； human translation ； comparable corpora ； CLAWS Part-of-speech Tagger

Abstract │ Reference (126) │ Altmetrics

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Machine translation (MT) has been advancing significantly in recent years. It is fast to run, easy to operate, and has become more human-like as techniques improve. However, its quality is still a concern and far from perfect. This research, therefore, employs a corpus-based approach, aiming to compare human and machine Chinese-to-English translations statistically at a deep and comprehensive textual level, including the aspects of lexical diversity, syntax, cohesion, etc., in order to identify which textual features can significantly indicate the differences between the human corpus and machine corpus, and to figure out possible explanations that might contribute to the improvement of MT output in the future. Such multilevel comparisons not only enable us to find out shortcomings of MT in detail, but also offer us insights on how to improve MT systems. Coh-Metrix 3.0, an automated text analysis tool, plays a major role in generating textual features for the four corpora that comprise an original Chinese corpus, a human corpus, a machine corpus, and a reference corpus, covering 8 domains, namely, contracts, patents, governments, literature, law, finance, environment, as well as science and technology. Results obtained from Coh-Metrix 3.0 are compared using t-tests in StatPlus, as well as further processed and analyzed by CLAWS Part-of-speech Tagger, AntConc and Log-likelihood Calculator. The research findings show human and machine translations are significantly different from each other with respect to basic textual features, readability and syntax; long and nonsense sentences can be commonly seen in machine translations because machine translation systems are not able to insert punctuation marks into sentences based on semantics; prepositions or prepositional phrases mainly account for an unexpected result that human translations contain significantly more words than machine translations; the performance of machine translation systems varies in accordance with text type; besides, lexical diversity was found to be associated with textual cohesion. This thesis will elaborate on the findings and further provide feasible suggestions for future research.

Reference ( 126 ) 〈TOP〉

Allen, J. (2003). Post-editing. Benjamins Translation Library, 35, 297-318.
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ALPAC Report (1966). Languages and machines: computers in translation and linguistics. A report by the Automatic Language Processing Advisory Committee, Division of Behavioral Sciences, National Academy of Sciences, National Research Council. (Publication 1416).
連結：
Anazawa, R., Ishikawa, H., Park, M. J., & Kiuchi, T. (2013). Online machine translation use with nursing literature: evaluation method and usability. Computers Informatics Nursing, 31(2), 59-65.
連結：
Baker, M. (1993). Corpus linguistics and translation studies: Implications and applications. Text and Technology: In Honour of John Sinclair, 233, 250.
連結：
Baker, M. (1996). Corpus-based translation studies. Terminology, LSP and Translation Benjamins Translation Library Studies in Language Engineering in Honour of Juan C. Sager, 175.
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