A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces

Understand

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 consequently been proposed.

• However, existing models and studies (1) operate on under-specified and mutually differing bias definitions, (2) are tailored for a particular bias (e.g., gender bias) and (3) have been evaluated inconsistently and non-rigorously.
• In this work, we introduce a general framework for debiasing word embeddings.
• We operationalize the definition of a bias by discerning two types of bias specification: explicit and implicit.