一句话：提出指称透明输入（RTI）方法并实现 Purity，用于检测机器翻译错误，在 Google 与 Bing 上高精度发现大量误译。
机器翻译在神经网络推动下快速进步，但因模型复杂且难以解释，仍不鲁棒，可能产生错误翻译，导致误解、经济损失、人身安全与健康威胁乃至政治冲突。本文提出指称透明输入（RTIs）：同一段文本在不同语境中应得到相似翻译；若翻译破坏该性质，就可能是错误。作者实现工具 Purity，用其自动检测此类违反。为评估 RTI，作者用 200 个无标签句子测试 Google Translate 和 Bing Microsoft Translator，分别检出 123 和 142 个错误翻译，精度达 79.3% 和 78.3%。错误类型多样，包括欠译、过译、词/短语误译、错误修饰和逻辑不清。
Machine translation software has seen rapid progress in recent years due to the advancement of deep Neural Networks. People routinely use machine translation software in their daily lives for tasks such as ordering food in a foreign restaurant, receiving medical diagnosis and treatment from foreign doctors, and reading international political news online. However, due to the complexity and intractability of the underlying Neural Networks, modern machine translation software is still far from robust and can produce poor or incorrect translations; this can lead to misunderstanding, financial loss, threats to personal safety and health, and political conflicts. To address this problem, we introduce referentially transparent inputs (RTIs), a simple, widely applicable methodology for validating machine translation software. A referentially transparent input is a piece of text that should have similar translations when used in different contexts. Our practical implementation, Purity, detects when this property is broken by a translation. To evaluate RTI, we use Purity to test Google Translate and Bing Microsoft Translator with 200 unlabeled sentences, which detected 123 and 142 erroneous translations with high precision (79.3% and 78.3%). The translation errors are diverse, including examples of under-translation, over-translation, word/phrase mistranslation, incorrect modification, and unclear logic.