Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods

Abstract

Abstract In this study, we employ autoregressive and neural machine translation models to process natural language. Our exploration of AI translation workflows encompasses three key aspects: the encoder-decoder framework, text feature representation, and translation derivation methodologies. Subsequently, we identify the core evaluation indices essential for assessing intelligent translation systems. An innovative translation model is constructed by amalgamating AI-driven and traditional English translation techniques. This model is scrutinized for its effectiveness through various lenses, including the quality of English-to-Chinese poetry translations and manual evaluations. Comparative analysis of the proposed translation method with other extant methods across diverse datasets reveals superior performance metrics: the BLEU score consistently exceeds 4.5 across all three test sets, and the METEOR score ranges between 4.3 and 4.6, surpassing competing methods. Additionally, translation accuracy for sentences of varying lengths in the source language is maintained between 85% and 96%. The objective of this research is to conduct a comprehensive examination of the integration of AI English translation with conventional methodologies, aiming to foster innovative theoretical contributions and practical advancements in the field.