论文标题
多语言神经机器翻译中的性别偏见：体系结构很重要
Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters
论文作者
论文摘要
多语言神经机器翻译体系结构主要不同于语言之间的共享模块和参数的数量。在本文中，从算法的角度来看，我们探讨了所选的体系结构在接受相同数据培训时是否会影响性别偏见的准确性。四个语言对的实验表明，特定于语言的编码器比共享编码器架构的偏差少。对来源嵌入的进一步可解释性分析和注意力表明，在特定于语言的情况下，嵌入式编码更多的性别信息，其注意力更加转移。两种行为都有助于减轻性别偏见。
Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspective, we explore if the chosen architecture, when trained with the same data, influences the gender bias accuracy. Experiments in four language pairs show that Language-Specific encoders-decoders exhibit less bias than the Shared encoder-decoder architecture. Further interpretability analysis of source embeddings and the attention shows that, in the Language-Specific case, the embeddings encode more gender information, and its attention is more diverted. Both behaviors help in mitigating gender bias.
