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Translating humor and internet memes is often one of the most challenging tasks in game localization. Jokes do not rely solely on words; they are rooted in cultural context, social background and the shared memories of a player community. A joke that lands in an English-speaking audience can fall flat in a Chinese environment. Finding a way to preserve the original work’s charm while making players in the target market genuinely laugh is a core problem localization teams must solve.
According to the latest 2025 Nimdzi survey, MTPE adoption surged by 75% over two years, rising from 26% in 2022 to 46% in 2024. However, widespread adoption has not produced uniform efficiency gains. Implementation outcomes vary significantly between organizations, indicating that a large portion of the potential productivity remains untapped.
With global collaboration and product globalization accelerating, technical documentation translation has become essential in cross-border projects. Beyond linguistic accuracy, it requires mastery of the technology, usage scenarios, and cultural context—especially in Japan, where precision and standardization are paramount. This article, drawing on a Glodom Chinese–Japanese project, explores how to deliver accurate technical content while adapting it to local usage and expectations.
In the world of game localization, even a short phrase on a button or a seemingly minor quest hint can shape a player’s first impression. These small but crucial strings—UI text, prompts, achievement names, and more—serve as the bridge between the game and its players. They must convey information accurately while appearing natural in every language and interface, making players feel as if the game was created just for them.
The rapid advancement of artificial intelligence is reshaping the translation industry, evolving from early rule-based systems to today’s neural network models. Machine translation has made a qualitative leap: tools like Google Translate and DeepL have progressed from rigid literal output to fluent expression. At the same time, AI has cut translation costs by more than 50% and improved processing speeds by hundreds of times.
