Human Translator vs Machine Translation. A Case Study in Turkish-Italian Lexicography

This paper presents a comparative analysis between human translation and automated outputs within the field of Turkish-Italian lexicography. Using a corpus of example sentences extracted from Turklex—a developing online multilingual dictionary—translations were carried out manually by a professional translator and subsequently generated by four different machine translation tools: Google Translate, DeepL, Gemini, and ChatGPT-4. Particular attention is paid to the rendering of headwords, a critical element in lexicographic accuracy. By examining the divergences and overlaps across these translations, the study highlights the strengths and limitations of current NMTs and AI-driven chatbots when applied to a less commonly examined language pair. To the best of our knowledge, this is the first focused investigation into Turkish-Italian translation within a lexicographical framework. The findings aim to inform both the evaluation of machine translation tools and the broader discussion on their integration into language resource development. The research found that while ChatGPT slightly outperformed the other tools, AI chatbots and NMTs still fall short of human translators in delivering translations that are both contextually nuanced and semantically accurate.