论文标题
语法性别关联超过跨语言嵌入中的局部性别偏见
Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings
论文作者
论文摘要
最近的研究表明，语义的矢量空间模型可以反映人类文化中的不良偏见。我们对跨语词嵌入的研究表明，局部性别偏见与语法性别关联的效果相互作用，并超过了大小，并且两者都可以通过语料库捕捉来减弱。这一发现对下游应用程序（例如机器翻译）有影响。
Recent research has demonstrated that vector space models of semantics can reflect undesirable biases in human culture. Our investigation of crosslinguistic word embeddings reveals that topical gender bias interacts with, and is surpassed in magnitude by, the effect of grammatical gender associations, and both may be attenuated by corpus lemmatization. This finding has implications for downstream applications such as machine translation.
