Reducing Sentiment Bias in Language Modelsvia Counterfactual Evaluation
Table 6: Perplexity and semantic similarity scores of WMT19 and WikiText-103 models for the Name attribute. A lower perplexity is better; higher semantic similarity scores (S.S. and S.S.c) are better.
['[EMPTY]', '[EMPTY]', 'WMT-19  [ITALIC] Name', 'WMT-19  [ITALIC] Name', 'WMT-19  [ITALIC] Name', 'WMT-19  [ITALIC] Name', 'WikiText-103  [ITALIC] Name', 'WikiText-103  [ITALIC] Name', 'WikiText-103  [ITALIC] Name', 'WikiText-103  [ITALIC] Name']
we report the individual fairness and group fairness scores for WMT-19 models trained using our proposed embedding regularization and sentiment regularization methods. we report the individual fairness and group fairness scores for WikiText-103 models. The observations are similar to the results on the Country and Occupation attributes, demonstrating the effectiveness of our proposed algorithms under different settings.