Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Table 3: Tag-level precision, recall, F1 score, and accuracy and form-level accuracy for the baselines (“–BASE”) and for our approach (“–LIN” is the linear parameterization, “–NN” is the neural parameterization).
['[EMPTY]', '[BOLD] Tag  [BOLD] P', '[BOLD] Tag  [BOLD] R', '[BOLD] Tag  [ITALIC]  [BOLD] F [BOLD] 1', '[BOLD] Tag  [BOLD] Acc', '[BOLD] Form  [BOLD] Acc']
Recall is consistently significantly lower than precision. As expected, the baselines have the highest precision (though not by much). This is because they reflect well-known rules for each language. That said, they have lower recall than our approach because they fail to capture more subtle relationships. The grammaticality of the corpora following CDA differs between languages. That said, with the exception of Hebrew, our approach either sacrifices less grammaticality than naïve swapping of gendered words and sometimes increases grammaticality over the original corpus.