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Anaphora resolution (coreference) systems designed for the CONLL 2012 datasettypically cannot handle key aspects of the full anaphora resolution task suchas the identification of singletons and of certain types of non-referringexpressions (e.g., expletives), as these aspects are not annotated in thatcorpus. However, the recently released dataset for the CRAC 2018 Shared Taskcan now be used for that purpose. In this paper, we introduce an architectureto simultaneously identify non-referring expressions (including expletives,predicative s, and other types) and build coreference chains, includingsingletons. Our cluster-ranking system uses an attention mechanism to determinethe relative importance of the mentions in the same cluster. Additionalclassifiers are used to identify singletons and non-referring markables. Ourcontributions are as follows. First all, we report the first result on the CRACdata using system mentions; our result is 5.8% better than the shared taskbaseline system, which used gold mentions. Second, we demonstrate that theavailability of singleton clusters and non-referring expressions can lead tosubstantially improved performance on non-singleton clusters as well. Third, weshow that despite our model not being designed specifically for the CONLL data,it achieves a score equivalent to that of the state-of-the-art system by Kantorand Globerson (2019) on that dataset.

A Cluster Ranking Model for Full Anaphora Resolution