Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures

An assential aspect of structured predic- tion is the evaluation of an output struc- ture against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effec- tive loss functions. In this paper, we trade off exact computation for enabling the use of more complex loss functions for coreference resolution (CR). Most note- worthily, we show that such functions can be (i) automatically learned also from controversial but commonly accepted CR measures, e.g., MELA, and (ii) success- fully used in learning algorithms. The ac- curate model comparison on the standard CoNLL–2012 setting shows the benefit of more expressive loss for Arabic and En- glish data.