论文标题
公平嵌入引擎：用于分析和减轻单词嵌入性别偏见的库
Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings
论文作者
论文摘要
已显示非上下文单词嵌入模型从培训语料库中继承了性别，种族和宗教的类似人类刻板印象的偏见。为了解决这个问题，已经出现了大量的研究，旨在减轻这些偏见，同时保持嵌入的句法和语义效用。本文介绍了公平嵌入引擎（费用），这是一个用于分析和减轻单词嵌入性别偏见的库。费用结合了各种最先进的技术，用于量化，可视化和减轻标准抽象中单词嵌入性别偏见。费用将帮助从业人员对其嵌入模型中现有的偏差方法进行快速轨道分析。此外，它将通过评估其在一系列标准指标上的性能来快速原型制定新方法。
Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora. To counter this issue, a large body of research has emerged which aims to mitigate these biases while keeping the syntactic and semantic utility of embeddings intact. This paper describes Fair Embedding Engine (FEE), a library for analysing and mitigating gender bias in word embeddings. FEE combines various state of the art techniques for quantifying, visualising and mitigating gender bias in word embeddings under a standard abstraction. FEE will aid practitioners in fast track analysis of existing debiasing methods on their embedding models. Further, it will allow rapid prototyping of new methods by evaluating their performance on a suite of standard metrics.
