Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model

• URL: http://arxiv.org/abs/2511.02958v1
• Date: Tue, 04 Nov 2025 19:59:25 GMT
• Title: Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model
• Authors: Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez,
• Abstract summary: We propose a novel approach to distinguish between human and machine-translated sentences.<n> Experimental results show that our method outperforms current state-of-the-art techniques.
• Score: 4.750257527930005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern machine translation (MT) systems depend on large parallel corpora, often collected from the Internet. However, recent evidence indicates that (i) a substantial portion of these texts are machine-generated translations, and (ii) an overreliance on such synthetic content in training data can significantly degrade translation quality. As a result, filtering out non-human translations is becoming an essential pre-processing step in building high-quality MT systems. In this work, we propose a novel approach that directly exploits the internal representations of a surrogate multilingual MT model to distinguish between human and machine-translated sentences. Experimental results show that our method outperforms current state-of-the-art techniques, particularly for non-English language pairs, achieving gains of at least 5 percentage points of accuracy.

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