Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may arise from the widespread use of MT tools. Along this line, this paper offers a meticulous assessment of three commercial MT systems - Google Translate, DeepL, and Modern MT - with a specific focus on gender translation and bias. For three language pairs (English/Spanish, English/Italian, and English/French), we scrutinize the behavior of such systems at several levels of granularity and on a variety of naturally occurring gender phenomena in translation. Our study takes stock of the current state of online MT tools, by revealing significant discrepancies in the gender translation of the three systems, with each system displaying varying degrees of bias despite their overall translation quality.

翻译：机器翻译（MT）在质量上持续取得显著进步，并被日益广泛地大规模采用。因此，相关分析已转向更细微的方面、复杂的现象，以及MT工具广泛使用可能带来的潜在风险。沿此方向，本文对三种商业MT系统——谷歌翻译、DeepL和Modern MT——进行了细致的评估，特别聚焦于性别翻译与偏见问题。针对三种语言对（英语/西班牙语、英语/意大利语和英语/法语），我们在多个粒度层面以及翻译中各种自然出现的性别现象上，审视了这些系统的行为。本研究通过揭示三种系统在性别翻译上的显著差异，全面调查了当前在线MT工具的状态，尽管各系统整体翻译质量良好，但仍展现出不同程度的偏见。