Which coreference evaluation metric do you trust? A proposal for a link-based entity aware metric

Interpretability and discriminative power are the two most basic requirements for an evaluation metric. In this paper, we report the mention identification effect in the B3, CEAF, and BLANC coreference evaluation metrics that makes it impossible to interpret their results properly. The only metric which is insensitive to this flaw is MUC, which, however, is known to be the least discriminative metric. It is a known fact that none of the current metrics are reliable. The common practice for ranking coreference resolvers is to use the average of three different metrics. However, one cannot expect to obtain a reliable score by averaging three unreliable metrics. We propose LEA, a Link-based Entity-Aware evaluation metric that is designed to overcome the shortcomings of the current evaluation metrics. LEA is available as branch LEA-scorer in the reference implementation of the official CoNLL scorer.