论文标题
翻译系统对额外上下文的敏感程度如何？通过相关环境减轻神经机器翻译模型中的性别偏见
How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts
论文作者
论文摘要
基于变压器架构顶部建立的神经机器翻译系统通常会根据文字重叠度量标准来改善翻译质量的最新质量。但是，越来越多的研究还强调了这些模型在训练过程中所包含的固有性别偏见，这在翻译中反映了很差。在这项工作中，我们调查是否可以指示这些模型在推理期间使用有针对性的指导说明作为上下文来解决其偏见。通过在推理期间与输入一起翻译相关的上下文句子，我们观察到在三个流行的测试套件（Winomt，bug，SimpleGen）中，在减少翻译中的性别偏见方面有了很大的改进。我们进一步提出了一个新型指标，以评估其对在翻译过程中使用上下文以纠正其偏见的敏感性的几种大型预训练模型（Opus-Mt，M2M-100）。我们的方法不需要微调，因此可以轻松地在生产系统中使用，以从刻板印象的性别占领偏见中消除偏见的翻译1。我们希望我们的方法与我们的指标一起用于构建更好的无偏见的无偏见的翻译系统。
Neural Machine Translation systems built on top of Transformer-based architectures are routinely improving the state-of-the-art in translation quality according to word-overlap metrics. However, a growing number of studies also highlight the inherent gender bias that these models incorporate during training, which reflects poorly in their translations. In this work, we investigate whether these models can be instructed to fix their bias during inference using targeted, guided instructions as contexts. By translating relevant contextual sentences during inference along with the input, we observe large improvements in reducing the gender bias in translations, across three popular test suites (WinoMT, BUG, SimpleGen). We further propose a novel metric to assess several large pre-trained models (OPUS-MT, M2M-100) on their sensitivity towards using contexts during translation to correct their biases. Our approach requires no fine-tuning and thus can be used easily in production systems to de-bias translations from stereotypical gender-occupation bias 1. We hope our method, along with our metric, can be used to build better, bias-free translation systems.
