Corpus Construction and Translation Software Optimization Path for French Literature Translation

Abstract

Amid deepening global cultural exchange and rapid advances in translation technology, French literary translation still faces challenges such as metaphor distortion, insufficient literary expression, and inconsistent rendering of culturally loaded terms. Current translation software often fails to balance semantic accuracy and literary quality because of insufficient targeted corpus support and weak adaptation algorithms for literary translation. This study proposes a systematic path for corpus construction and translation software optimization tailored to French literature. A dedicated bilingual parallel corpus is constructed by integrating classic literary works, authoritative translations, and cultural annotations. The corpus contains one million original French words and 800,000 parallel words, covering major literary genres and schools including Classicism, Romanticism, Realism, Modernism, and Existentialism. Based on corpus data, software optimization algorithms are designed across four dimensions: vocabulary alignment, syntactic structure parsing, cultural image mapping, and literary style adaptation. The study provides corpus-based technical support for improving translation accuracy, cultural adaptability, and stylistic fidelity in French literary translation.