Abstract
Detecting the translation direction of parallel text has applications 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 $p(\text{translation}|\text{original})>p(\text{original}|\text{translation})$, motivated by the well-known simplification effect in translationese or machine-translationese. In experiments with massively 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. Code and demo are available at this https URL
Abstract (translated)
检测并平行文本的翻译方向在机器翻译训练和评估以及法庭应用（如检测抄袭或伪造）方面有应用，但还具有许多应用，如解决抄袭或伪造指控。在这项工作中，我们探讨了一种基于简单假设 $p(\text{translation}|\text{original})>p(\text{original}|\text{translation})$ 的无监督翻译方向检测方法，这是基于翻译语料库中众所周知的美化效应或机器翻译语料库中的简化效应。在20个翻译方向上使用大规模多语言机器翻译模型进行的实验中，我们证实了该方法对于高资源语言对的成功应用，达到每条文档82-96%的NMT产生的翻译准确度，以及60-81%的人际翻译准确度，具体取决于所使用的模型。代码和演示版本可在https://这个URL上获得。
URL
https://arxiv.org/abs/2401.06769
