论文标题
评估语音翻译中的性别偏见
Evaluating Gender Bias in Speech Translation
论文作者
论文摘要
科学界越来越意识到要接受多元化并始终代表主要和次要社会群体的必要性。当前，对于不同类型的偏见没有标准评估技术。因此，迫切需要提供评估集和协议来衡量我们自动系统中的现有偏见。评估偏见应该是减轻系统中它们的重要一步。 本文介绍了Winost，这是一个新的可免费获得的挑战集，用于评估语音翻译中的性别偏见。 Winost是Winomt的语音版本，它是MT挑战集，并且均遵循评估协议以衡量性别准确性。使用最新的端到端语音翻译系统，我们报告了四个语言对的性别偏见评估，我们表明语音翻译中的性别准确性比MT低23％以上。
The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT which is a MT challenge set and both follow an evaluation protocol to measure gender accuracy. Using a state-of-the-art end-to-end speech translation system, we report the gender bias evaluation on four language pairs and we show that gender accuracy in speech translation is more than 23% lower than in MT.
