Human-Machine Interaction Translation under Artificial Intelligence and Big Data: Analysis from the Perspective of Text Stratification and Corpus Construction

Abstract

With the continuous advancement of artificial intelligence and big data technologies, the neural network-based machine translation driven by deep learning continues to flourish. This ongoing progress not only propels the application of translation technology and reshapes the translation industry but also profoundly impacts the realms of language learning and translation. This paper, situated against the backdrop of artificial intelligence and big data, focuses on the fundamental aspects of text stratification and corpus construction. Building upon discussions about text stratification and corpus construction, this paper extensively examines how human and machine interaction can effectively balance translation quality and cost, maximizing translation efficiency. Additionally, this paper proposes several innovation suggestions regarding future bilingual translation pedagogy.