以半自動化譯後編輯修正專利摘要中譯英機器翻譯之錯誤

許隽安

doi:10.6342/NTU.2015.01302

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以半自動化譯後編輯修正專利摘要中譯英機器翻譯之錯誤

A Semi-Automatic Method for Correcting Errors in Chinese-English Machine Translations of Patent Abstracts

許隽安 , Masters　　Advisor：蔡毓芬　　

英文 DOI： 10.6342/NTU.2015.01302

中英機器翻譯；專利翻譯；語言歧異；錯誤分析；機器翻譯評鑑；譯後編輯； Chinese-English machine translation ； patent translation ； language divergence ； error analysis ； MT evaluation ； post-editing

Abstract │ Reference (59) │ Altmetrics

Abstract 〈TOP〉

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The grammatical structures of Chinese and English are very different. Such divergences are reflected in translation errors produced by machine translation (MT) systems between the two languages. It is therefore necessary to understand the effect such language divergences has on MT outputs. As there was a noticeable absence of such research project on machine translation systems of the Chinese-English language pair, this study presents results of an analysis of errors found in machine translated patent abstracts, and designed accordingly a semi-automatic method for post-editing targeting a specific group of errors.
In the first part of the study, 115 English machine-translated abstracts of Chinese patent abstracts were selected, all of which were done by Google Translate, a statistical machine translation (SMT) system developed for general usage. After errors in the translations were identified, they were categorized based on a hierarchical classification scheme with five categories at the first level: orthographic errors, morphological errors, lexical errors, semantic errors, and syntactic errors.
The distribution of these errors yields important insights about the difficulties encountered by the SMT system during the process of translation. Firstly, tokenization of the SL texts was found problematic, as there are no delimiters between words in the Chinese language. Secondly, assigning parts of speech to words in the source sentences was also found challenging, given that Chinese has no inflections for MT systems to identify parts of speech with, and that each Chinese word may act as a syntactic component of more than one part-of-speech (POS) category in different contexts. Thirdly, due to the different orders of sentence components in Chinese and English, erroneous syntactic orders appeared at high frequency, and were pertinent to the sentence lengths of source language sentences.
In the second part of this study, a semi-automatic method for post-editing was proposed. This method was targeted at semantically incorrect terms in MT. It consisted of three steps (lexical alignment, noun phrase extraction, and term substitution) and was proved to be able to increase the Bilingual Evaluation Understudy (BLEU) score of machine-translated texts. This study provides a descriptive basis for additional research, and has implications for MT developers as well as post-editors in refining and improving the quality of Chinese-English machine translation output.

Reference ( 59 ) 〈TOP〉

Allen, J. (2004, September 28-October 2). Case study: implementing MT for the translation of pre-sales marketing and post-sales software deployment documentation at Mycom International. Paper presented at the 6th Conference of the Association for Machine Translation in the Americas (AMTA 2004), Washington, DC, USA (pp. 1-6) Springer.
連結：
Barnett, J., Mani, I., & Rich, E. (1994). Reversible Machine Translation: What to Do When the Languages Don’t Match up. Reversible Grammar in Natural Language Processing (pp. 321-364): Springer.
連結：
Birch, A., Osborne, M., & Blunsom, P. (2010). Metrics for MT Evaluation: Evaluating Reordering. Machine Translation, 24(1), pp. 15-26.
連結：
Costa-Jussà, M. R., & Farrús, M. (2014). Statistical Machine Translation Enhancements through Linguistic Levels: A Survey. ACM Computing Surveys (CSUR), 46(3), pp. 413-431.
連結：
Hutchins, W. J. (1986). Machine Translation: past, present, future. UK: Ellis Horwood Chichester.
連結：

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