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quality. By exploring some translation universals measured by lexical and part-of-speech indexes in a variety of genres, the study showed that translation quality did not correlate with machine translationese. In an attempt to provide MT developers with

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the use of corpora, which deserves to be taken as a model. The authors conclude that the actual experiment they report, surprisingly, did not show that using the corpora made much difference to translation quality, but their work does illustrate the

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Zhiliang er Fendou (On Literary Translation and Translation Quality). Reprinted in Luo (ed.) 1984. 501-517. Morgan, B. Q. 1959. Bibliography: 46 BC - 1958. In R. A. Brower (ed.) 1959. 271

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issue of whether SFL can be applied to improve translation quality. The author followed the hypothesis of Mathiessen, according to whom “there is a high degree of ‘equivalence’ or congruence between languages as far as metafunctions are concerned” (p

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consequences of uncorrected errors, falling expectations of translation quality, the flattening of language, and the (unpaid?) use made of human translation as input for MT systems. From a utopian perspective, MT can be seen as basically “virtuous ”, because it

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, Quinci developed her own system, which was a combination of Mossop's classification of errors and Vollmar's severity scale. It is not known who identified the errors and what steps were taken to ensure (interrater) reliability. Results of translation

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proficiency and translation quality are excellent. Their translating styles are regarded as clearly distinctive: Lin Yutang's translation is regarded as far too free ( Ye, 1999 ) and is criticized for the discrepancies between ST and TT, while James Legge

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psychological basis for both pre-translation source text analysis and post-translation editing. Besides, it contributes to the ripening of creative thinking, or translator’s insight. Consequently, it is crucial for translation quality. In fact, recent studies

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and quickly achieved a higher translation quality than its predecessors’ rule-based machine translation (RBMT) and phrase-based statistical machine translation (PBSMT) (cf. Beyer and Burchardt 2017:23). As the name suggests, neural machine translation

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network-based translation (NMT) systems with translation quality approaching that of existing dominant systems. However, since the resources available at the time were not yet suitable for handling large data sets, none of these models could be trained

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