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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

February 1, 2023

Authors

Lei Ding

University of Alberta


Dengdeng Yu

University of Texas at Arlington


Jinhan Xie

University of Alberta


Wenxing Guo

University of Alberta


Shenggang Hu

University of Essex


Meichen Liu

University of Alberta


Linglong Kong

University of Alberta


Hongsheng Dai

University of Essex


Yanchun Bao

University of Essex


Bei Jiang

University of Alberta


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Special Track on AI for Social Impact

Downloads:

Download PDF

Abstract:

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.

DOI:

10.1609/aaai.v36i11.21443


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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