Abstract This study examines the performance of various machine translation (MT) systems and a generative AI (GenAI) translation in the context of game text translation using data from the popular racing game Need for Speed: Unbound ( Criterion Games 2022 ). Focusing on factors such as ambiguity, contextual disambiguation, stylistic variations, domain specific knowledge, and technical entities (e.g. placeholders), the analysis compares human translations with outputs from Google Translate, DeepL, Papago, and ChatGPT. Three representative examples from a game script are examined, revealing that while MT and GenAI systems can preserve lexical content, they often fail to capture critical nuances and contextual meanings that are crucial for interactive gaming environments. The findings highlight the need for integrating additional contextual information and domain-specific post-editing to improve MT and GenAI translation quality for translated game texts, contributing to the broader discussion on enhancing interactive media translation.
Kim et al. (Thu,) studied this question.
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