  In order for coreference resolution systems to be useful in practice, they
must be able to generalize to new text. In this work, we demonstrate that the
performance of the state-of-the-art system decreases when the names of PER and
GPE named entities in the CoNLL dataset are changed to names that do not occur
in the training set. We use the technique of adversarial gradient-based
training to retrain the state-of-the-art system and demonstrate that the
retrained system achieves higher performance on the CoNLL dataset (both with
and without the change of named entities) and the GAP dataset.
