Experts and professionals in specialized fields often need writing tools
to communicate in English as a means to disseminate their knowledge or enter
the international market. There are different tools to accomplish this and most of
them are, lately, Machine Translation systems (MT) based on Neural Machine
Translation (NMT), an approach using artificial neural networks to translate with
outstanding fluency. Free and open systems such as Google Translate or, more
recently, ChatGPT used as a translator, have popularized NMT to a multitude of
users. However, there are experts and professionals who, due to their lack of
command of English, often fail in their communication tasks by accepting NMT
system’s output as correct. This paper examines these systems’ performance
when translating terminology of the discourse in wine and olive oil tasting notes,
specifically from Spanish into English. This domain may serve to represent lessstudied
specialized languages where general language words and terms become
closely intertwined. The aim is to determine whether these systems can translate
terminology accurately within the domain, and, if so, whether the GPT-3.5 model
outperforms Google Translate. Results will help identify or discard possible language
solutions for users who need to obtain texts in specialized English with
professional and internationalization purposes, but who do not have the linguistic
or economic resources to ensure the quality of the English text. Results show that,
although ChatGPT yields fewer terminological errors than Google Translate in
terms of error severity and number of samples affected, professionals cannot rely
solely on these tools just yet.
