Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
This work addresses the problem of inconsistent bias analysis in NLP for researchers and practitioners, providing a foundational study that is incremental in scope.
The paper systematically analyzes biases in distributional word vectors across languages, corpora, and embedding models, finding that biases can be emphasized or downplayed by different models and that user-generated content may be less biased than encyclopedic text.
Word embeddings have recently been shown to reflect many of the pronounced societal biases (e.g., gender bias or racial bias). Existing studies are, however, limited in scope and do not investigate the consistency of biases across relevant dimensions like embedding models, types of texts, and different languages. In this work, we present a systematic study of biases encoded in distributional word vector spaces: we analyze how consistent the bias effects are across languages, corpora, and embedding models. Furthermore, we analyze the cross-lingual biases encoded in bilingual embedding spaces, indicative of the effects of bias transfer encompassed in cross-lingual transfer of NLP models. Our study yields some unexpected findings, e.g., that biases can be emphasized or downplayed by different embedding models or that user-generated content may be less biased than encyclopedic text. We hope our work catalyzes bias research in NLP and informs the development of bias reduction techniques.