机器翻译模型是翻译方向的零样本检测器
Machine Translation Models are Zero-Shot Detectors of Translation Direction
AI总结：
本研究提出一种基于翻译简化效应的无监督翻译方向检测方法，利用多语言机器翻译模型验证了其在高资源语言对上的有效性，对机器翻译和人工翻译均取得较高准确率。
摘要：
检测平行文本的翻译方向在机器翻译训练和评估中有应用，在解决抄袭或伪造指控等取证方面也有应用。在本研究中，我们探索了一种无监督的翻译方向检测方法，该方法基于简单假设$p(\ ext{translation}|\ ext{original})>p(\ ext{original}|\ ext{translation})$，其动机是翻译体或机器翻译体中众所周知的简化效应。在使用大规模多语言机器翻译模型对20个翻译方向进行的实验中，我们证实了该方法对高资源语言对的有效性，根据所用模型的不同，对NMT生成的翻译达到了82--96%的文档级准确率，对人工翻译达到了60--81%的准确率。代码和演示可在https://github.com/ZurichNLP/translation-direction-detection获取。
英文摘要：
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 https://github.com/ZurichNLP/translation-direction-detection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
