使用代理多语言翻译模型的自动机器翻译检测
Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model
AI总结：
提出利用代理多语言MT模型内部表示区分人类与机器翻译的新方法，在非英语语言对上准确率至少提升5个百分点。
摘要：
现代机器翻译（MT）系统依赖大型平行语料库，这些语料库通常从互联网收集。然而，近期证据表明：(i) 这些文本中有很大一部分是机器生成的翻译，(ii) 训练数据中过度依赖此类合成内容会显著降低翻译质量。因此，过滤非人类翻译正成为构建高质量MT系统的重要预处理步骤。在本工作中，我们提出一种新方法，直接利用代理多语言MT模型的内部表示来区分人类翻译和机器翻译的句子。实验结果表明，我们的方法优于当前最先进的技术，特别是在非英语语言对上，准确率至少提高了5个百分点。
英文摘要：
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.
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。
