Work

Evaluation of translations into Plain German produced by humans and MT systems including ChatGPT

Year: 2025

Type: article

Abstract: In this paper, we present the results of a study evaluating intralingual machine translations of health information texts into Plain German. In our study, we compare machine-translated simplified texts with those simplified manually by human translators, as well as with the original, unsimplified texts. We compare the output of four different machine translations systems and assess the translation quality using various criteria, including translation errors, readability, and syntactic complexity. The study reveals that from the four analysed machine translation systems, ChatGPT performed worst. Our results also suggest that fine-tuning a model with task-specific and domain-specific data improves the translation quality.

Source: SKASE Journal of translation and interpretation.

DOI: 10.33542/jti2025-s-3

Authors: Sarah Ahrens, Silvana Deilen, Sergio Hernández Garrido, Ekaterina Lapshinova-Koltunski

Institution: University of Hildesheim

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4414723580

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