  We present an effective system adapted from the end-to-end neural coreference
resolution model, targeting on the task of anaphora resolution in dialogues.
Three aspects are specifically addressed in our approach, including the support
of singletons, encoding speakers and turns throughout dialogue interactions,
and knowledge transfer utilizing existing resources. Despite the simplicity of
our adaptation strategies, they are shown to bring significant impact to the
final performance, with up to 27 F1 improvement over the baseline. Our final
system ranks the 1st place on the leaderboard of the anaphora resolution track
in the CRAC 2021 shared task, and achieves the best evaluation results on all
four datasets.
