An interpretable machine learning framework for classifying human and machine translations across genres

Neural modeling and large language models (LLMs) has led to a significant improvement in the quality of machine translation (MT) output. While MT increasingly rivals human output, “translationese”—systematic linguistic fingerprints left by the translation process—has long been studied qualitatively,...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Lingxi Fan, Hongyang Du, Gan Huang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2026-06-01
coleção:	Frontiers in Artificial Intelligence
Assuntos:	machine and human translation machine learning SHapley Additive exPlanations (SHAP) text classification translationese
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/frai.2026.1813227/full
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!