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arXiv · 2511.02958

Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model

Cristian García-Romero·Miquel Esplà-Gomis·Felipe Sánchez-Martínez
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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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Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez. 2025-11-04. Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model. https://arxiv.org/abs/2511.02958

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