We tackle the task of automatically discriminating between human and machine translations. As opposed to most previous work, we perform experiments in a multilingual setting, considering multiple languages and multilingual pretrained language models. We show that a classifier trained on parallel data with a single source language (in our case German-English) can still perform well on English translations that come from different source languages, even when the machine translations were produced by other systems than the one it was trained on. Additionally, we demonstrate that incorporating the source text in the input of a multilingual classifier improves (i) its accuracy and (ii) its robustness on cross-system evaluation, compared to a monolingual classifier. Furthermore, we find that using training data from multiple source languages (German, Russian, and Chinese) tends to improve the accuracy of both monolingual and multilingual classifiers. Finally, we show that bilingual classifiers and classifiers trained on multiple source languages benefit from being trained on longer text sequences, rather than on sentences.

翻译：我们聚焦于自动区分人类译文与机器译文的判别任务。与以往多数研究不同，我们在多语言环境下展开实验，涉及多种语言及多语言预训练语言模型。研究表明，基于单一源语言（本文为德-英方向）平行数据训练的分类器，能够有效判别来自不同源语言的英语译文，即使机器译文由非训练系统生成。此外，我们验证了在多语言分类器的输入中融入源文本，相较于单语言分类器可提升（i）分类准确度与（ii）跨系统评估的鲁棒性。进一步发现，采用多源语言（德语、俄语、汉语）训练数据能显著提升单语言与多语言分类器的性能。最后，我们证明双语分类器及多源语言训练的分类器在长文本序列（而非单句）训练中获益更大。