A new artificial intelligence, or AI, translation model could improve the accuracy and cultural appropriateness of Chinese-English public signs in tourist attractions, according to research in the International Journal of Environmental Technology and Management. The approach treats translation as more than a word-for-word conversion and combines machine translation with principles from eco-translatology in which language, culture, and social context are considered.
The researchers have incorporated these principles into a neural translation system based on transformer architecture, a widely used AI system for processing relationships between words in a sentence. The model thus uses cultural-language databases together with sentiment analysis. The latter uses a computer to assess the emotional or evaluative language in a piece of text. The system can then tweak the translation for linguistic, cultural, and communicative context.
In tests, the team reports an improvement over older approaches for cultural adaptability, fluency, and completeness. The findings suggest that translation systems designed for specific functions may be better suited to public-facing texts where a culturally inappropriate phrase might confuse visitors or change the intended message. The same system could support multilingual urban signs, heritage-site interpretation, and educational notices.
At this time, the model is limited to a single language pair and a corpus concentrated on Chinese scenic areas. However, the researchers plan to expand the training data and incorporate knowledge graphs, situational modelling, and causal reasoning to improve the system’s ability to be used in different cultures and settings. There is also the potential to improve the handling of historical references, deeper cultural meanings, and linguistic variation by developing deeper reasoning rather than relying on simple rules and templates.
Zhang, C. and Wang, Y. (2026) ‘Multimedia artificial intelligence technology for accurate translation of scenic area public notices from Chinese to English in ecological translation studies’, Int. J. Environmental Technology and Management, Vol. 29, No. 7, pp.1–22.
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