Translation Strategies for AI Agreements from the Perspective of Functional Equivalence: A Case Study of ChatGPT

Abstract

This research aims to deeply explore the translation strategies of artificial intelligence agreement texts from the perspective of functional equivalence, and conducts a detailed case analysis with ChatGPT as the research object. With the increasing global AI cooperation, such as OpenAI's API partnerships, the demand for AI agreement translation has increased sharply, but there are still many challenges in achieving accuracy and functional equivalence in the translation process. The research comprehensively uses the literature research method and case analysis method to deeply analyze the translation strategies of ChatGPT at the lexical, syntactic, and discourse levels. The study finds that ChatGPT performs well in the accurate translation of technical terms and other levels, but there is still much room for improvement in sentence structure adjustment and discourse coherence. Based on this, this research proposes improvement strategies such as algorithm optimization, corpus expansion, and human intervention, in order to provide solid theoretical and practical references for the development of AI agreement text translation, effectively improve translation quality, and promote exchanges and cooperation in the global AI field.