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Previous attempts to incorporate a mention detection step into end-to-endneural coreference resolution for English have been hampered by the lack ofsingleton mention span data as well as other entity information. This paperpresents a coreference model that learns singletons as well as features such asentity type and information status via a multi-task learning-based approach.This approach achieves new state-of-the-art scores on the OntoGUM benchmark(+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3points on average), likely due to greater generalizability for mentiondetection and utilization of more data from singletons when compared to onlycoreferent mention pair matching.

Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization