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Sentiment Sensitive Debiasing: A Learning-Based Approach to Remove Ethnic Stereotypes in Word Embeddings
Audhav N Durai, Aditya Vasantharao, Sauman Das
Pages - 26 - 35     |    Revised - 31-08-2022     |    Published - 01-10-2022
Volume - 13   Issue - 3    |    Publication Date - October 2022  Table of Contents
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KEYWORDS
Natural Language Processing, Bias Mitigation, Deep Learning, Word2Vec, Sentiment Analysis.
ABSTRACT
Word vectorization models are used to represent vocabulary in a vector space in a manner that captures semantic relationships between words. However, the state-of-the-art word vectorization models are shown to contain biases in their word embeddings due to ethnic prejudices and under representation in the corpora they are trained on. This paper proposes a novel sentiment sensitive, learning-based debiasing algorithm for multiclass bias mitigation. In this study, this algorithm is used for ethnic debiasing in CBOW Word2Vec models. Unlike other debiasing algorithms, this methodology accounts for the fact that not all ethnic correlations are biased and proper debiasing should also preserve unbiased ethnic information, such as cultural knowledge. Furthermore, it does not require a pre-defined, finite set of correlations to perform debiasing. Rather, models are penalized for making ethnic correlations towards non-neutral words and are allowed to make ethnic correlations towards neutral words, performing a thorough debiasing without losing ethnic knowledge. This study also proposes a new metric to evaluate bias called SMAC (Sentiment-Aware Mean Average Cosine Similarity) which accounts for sentiment in bias measurement. We train both the baseline and debiased CBOW models on the WikiCorpus. The Debiased model achieved are duction in bias by39.48% using the S-MAC metric in comparison to the baseline model.
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Mr. Audhav N Durai
Thomas Jefferson High School for Science and Technology, Alexandria, 22312 - United States of America
2023adurai@tjhsst.edu
Mr. Aditya Vasantharao
Thomas Jefferson High School for Science and Technology, Alexandria, 22312 - United States of America
Mr. Sauman Das
Thomas Jefferson High School for Science and Technology, Alexandria, 22312 - United States of America


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