OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Table S1: Comparison of bias expressed when using two different commonly used datasets - SNLI and MNLI - for natural language inference .
['Embedding', 'Net Neutral', 'Fraction Neutral', 'Threshold (0.5)', 'Threshold (0.7)']
For training the embeddings in the task of textual entailment, we use the SNLI snli dataset. While MultiNLI contains more complex and diverse sentences than SNLI, making it a good contender for training entailment tasks, we observed that MultiNLI also carries signifiantly more gender bias. Over 90% of sentence pairs that should have neutral associations are classified incorrectly using both GloVe and RoBERTa when trained using MultiNLI whereas using SNLI yields less than 70% biased and incorrect classifications on the same dataset. Since we focus on the representational bias in word embeddings and not on the bias in the datasets used in different machine learning tasks, using MultiNLI here would interfere with our focus. Moreover, MultiNLI has more complex sentences than SNLI, which means that there is a larger probability of having confounding noise. This would in turn weaken the implication of an incorrect inference of bias expressed. We compare here the amount of gender bias contained by the templates in SNLI and MultiNLI. While MultiNLI has more complex sentences, it also contains more bias as seen in Since our work attempts to understand and mitigate the bias in language representation and not the bias in data used in various tasks, we restrict our experiments to SNLI which expresses significantly less bias that MultiNLI.