Abstract
As a formal style, English for Science and Technology (EST) has characteristic of preciseness and terseness. For this reason, various clauses, especially attributive clauses, are often used in EST texts. Attributive clauses account for a large proportion of long sentences in EST, and cause difficulties in EST translation. The new generation of neural machine translation (NMT) has been greatly improved in translation quality, but it still has problems in translating English attributive clauses into Chinese. As a new branch of linguistics, language typology aims at describing and explaining the commonness and structural diversity of languages. It provides a new perspective to evaluate the quality of machine translation (MT) of attributive clauses in EST. This paper selects a certain proportion of examples from EST textbooks to make a comparative analysis between Google MT and manual translation under the guidance of linguistic typology. The goal of this paper is to enrich the existing research on EST translation in theory, to inspire translators to predict and correct possible errors in the output, and finally to improve the accuracy and efficiency of MT.
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Huang, H., Duan, H. (2023). On English-Chinese Machine Translation Evaluation in Translating Attributive Clauses in Scientific Texts. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1812. Springer, Singapore. https://doi.org/10.1007/978-981-99-2446-2_34
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DOI: https://doi.org/10.1007/978-981-99-2446-2_34
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