论文标题
无监督的检测情境化嵌入偏见与意识形态的应用
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology
论文作者
论文摘要
我们提出了一种完全无监督的方法，以检测上下文化嵌入中的偏见。该方法利用由社交网络延迟编码的分类信息，并结合了正交性正则化，结构化的稀疏学习和图形神经网络，以查找嵌入的子空间捕获此信息。作为一个具体的例子，我们专注于意识形态偏见的现象：我们介绍了意识形态子空间的概念，展示如何通过将我们的方法应用于在线讨论论坛上，以及目前的技术来探究它。我们的实验表明，意识形态子空间编码抽象的评估语义，并反映了唐纳德·特朗普（Donald Trump）担任总统期间政治左右光谱的变化。
We propose a fully unsupervised method to detect bias in contextualized embeddings. The method leverages the assortative information latently encoded by social networks and combines orthogonality regularization, structured sparsity learning, and graph neural networks to find the embedding subspace capturing this information. As a concrete example, we focus on the phenomenon of ideological bias: we introduce the concept of an ideological subspace, show how it can be found by applying our method to online discussion forums, and present techniques to probe it. Our experiments suggest that the ideological subspace encodes abstract evaluative semantics and reflects changes in the political left-right spectrum during the presidency of Donald Trump.
