34 citations · 103 across the 67 of their papers we have counts for
3 papers · 1 filter
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Anne Lauscher, Goran Glavaš, Simone Paolo Ponzetto +1
Distributional word vectors have recently been shown to encode many of the human biases, most notably gender and racial biases, and models for attenuating such biases have conseque…
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
Anne Lauscher, Ivan Vulić, Edoardo Maria Ponti +2
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional stat…
Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
Anne Lauscher, Goran Glavaš
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 d…