Web-driven vs. Machine Translation in the Medical Field
Web-driven vs. Machine Translation in the Medical Field

Summary/Abstract: The web as corpus is considered helpful in translation training, although the literature argues that Internet data might be too chaotic and badly organised. For this reason, web concordancers are often resorted to in order to carry out web searches as they are more linguistically oriented and user-friendly. At the same time, machine translation (MT) has made huge strides forward in the last decades, and scholars have increasingly focused on MT in technical translation training. However, so far little has been investigated on the quality of MT vis-à-vis translations via web concordancers. This paper wishes to bridge this gap and aims at exploring whether and to what extent the web-based translation of a medical article carried out by consulting the Leeds web-concordancer is qualitatively more satisfactory than a translation using MT, or vice-versa. To this aim, a classroom observation study is undertaken with 9 Italian final-year bachelor's students. The students' Leeds-driven translations are compared with the automatic translations performed by two MT tools (DeepL and MateCaT). The paper findings highlight the overall correctness of the machine-translated sentence structures as well as of the related grammar. However, MT presents lexical and collocational issues mostly due to infrequent word usages. By contrast, the web-driven students' translations present a wider variety of lexical and collocational choices, despite some grammatical shortcomings. The paper suggests further research to investigate whether more training in translations based on web concordancers can help students feel at ease with online searches and reduce inaccuracies.

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