  As language models are increasingly included in human-facing machine learning
tools, bias against demographic subgroups has gained attention. We propose
FineDeb, a two-phase debiasing framework for language models that starts with
contextual debiasing of embeddings learned by pretrained language models. The
model is then fine-tuned on a language modeling objective. Our results show
that FineDeb offers stronger debiasing in comparison to other methods which
often result in models as biased as the original language model. Our framework
is generalizable for demographics with multiple classes, and we demonstrate its
effectiveness through extensive experiments and comparisons with state of the
art techniques. We release our code and data on GitHub.
