Work

Machine Translation Models are Zero-Shot Detectors of Translation Direction

Year: 2025

Type: conference-paper

Abstract: Detecting the translation direction of parallel text is useful not only for machine translation training and evaluation but also has forensic applications, such as resolving plagiarism or forgery allegations.In this work, we explore an unsupervised approach to translation direction detection based on the simple hypothesis that p(translation|original) > p(original|translation), motivated by the wellknown simplification effect in translationese or machine-translationese.In experiments with multilingual machine translation models across 20 translation directions, we confirm the effectiveness of the approach for high-resource language pairs, achieving document-level accuracies of 82-96% for NMT-produced translations, and 60-81% for human translations, depending on the model used. 1

DOI: 10.18653/v1/2025.findings-acl.59

Authors: Michelle Wastl, Jannis Vamvas, Rico Sennrich

Institution: University of Zurich

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Awards: Document Synchronization Across Languages, Multitask Learning with Multilingual Resources for Better Natural Language Understanding

OpenAlex ID: W4412888935