论文标题
部分可观测时空混沌系统的无模型预测
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
论文作者
论文摘要
感官嵌入学习方法学习不同歧义词的不同感官的不同嵌入。一种模棱两可的词可能会在社会上有偏见，而其其他感官仍然没有偏见。与评估识别单词嵌入中社会偏见的众多先前的工作相比，在有义务嵌入中的偏见相对研究了。我们创建了一个基准数据集，用于评估有意义的嵌入中的社会偏见，并提出新颖的特定理解偏见评估措施。我们对使用拟议的措施对多种类型的社会偏见进行多种静态和上下文化的感觉嵌入进行了广泛的评估。我们的实验结果表明，即使在文字级别没有发现偏见的情况下，仍然存在着有意义的社会偏见的令人担忧的水平，这通常被单词级别的偏见评估指标所忽略。
Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word. One sense of an ambiguous word might be socially biased while its other senses remain unbiased. In comparison to the numerous prior work evaluating the social biases in pretrained word embeddings, the biases in sense embeddings have been relatively understudied. We create a benchmark dataset for evaluating the social biases in sense embeddings and propose novel sense-specific bias evaluation measures. We conduct an extensive evaluation of multiple static and contextualised sense embeddings for various types of social biases using the proposed measures. Our experimental results show that even in cases where no biases are found at word-level, there still exist worrying levels of social biases at sense-level, which are often ignored by the word-level bias evaluation measures.
