Skip to yearly menu bar Skip to main content


Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology

Valentin Hofmann · Janet Pierrehumbert · Hinrich Sch├╝tze

Hall E #133

Keywords: [ SA: Everything Else ] [ APP: Language, Speech and Dialog ]


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.

Chat is not available.