These suggest a near-equal distribution of feminine and masculine target pronouns (he/him/his vs. she/her) as well as an equal distribution of the two labels, which keeps chance-based performance at 50% expected accuracy.
with respect to our setting, several errors can be made by these existing models: predicting that the two entities and the pronoun share a similar cluster (Both Antecedents Predicted), that none of the two candidates shares a cluster with the pronoun (No Decision), or creating a cluster that contains the pronoun with the wrong candidate (Incorrect Decision).