  With widening deployments of natural language processing (NLP) in daily life,
inherited social biases from NLP models have become more severe and
problematic. Previous studies have shown that word embeddings trained on
human-generated corpora have strong gender biases that can produce
discriminative results in downstream tasks. Previous debiasing methods focus
mainly on modeling bias and only implicitly consider semantic information while
completely overlooking the complex underlying causal structure among bias and
semantic components. To address these issues, we propose a novel methodology
that leverages a causal inference framework to effectively remove gender bias.
The proposed method allows us to construct and analyze the complex causal
mechanisms facilitating gender information flow while retaining oracle semantic
information within word embeddings. Our comprehensive experiments show that the
proposed method achieves state-of-the-art results in gender-debiasing tasks. In
addition, our methods yield better performance in word similarity evaluation
and various extrinsic downstream NLP tasks.
