  Pronoun Coreference Resolution (PCR) is the task of resolving pronominal
expressions to all mentions they refer to. Compared with the general
coreference resolution task, the main challenge of PCR is the coreference
relation prediction rather than the mention detection. As one important natural
language understanding (NLU) component, pronoun resolution is crucial for many
downstream tasks and still challenging for existing models, which motivates us
to survey existing approaches and think about how to do better. In this survey,
we first introduce representative datasets and models for the ordinary pronoun
coreference resolution task. Then we focus on recent progress on hard pronoun
coreference resolution problems (e.g., Winograd Schema Challenge) to analyze
how well current models can understand commonsense. We conduct extensive
experiments to show that even though current models are achieving good
performance on the standard evaluation set, they are still not ready to be used
in real applications (e.g., all SOTA models struggle on correctly resolving
pronouns to infrequent objects). All experiment codes are available at
https://github.com/HKUST-KnowComp/PCR.
