  Entity Coreference Resolution is the task of resolving all mentions in a
document that refer to the same real world entity and is considered as one of
the most difficult tasks in natural language understanding. It is of great
importance for downstream natural language processing tasks such as entity
linking, machine translation, summarization, chatbots, etc. This work aims to
give a detailed review of current progress on solving Coreference Resolution
using neural-based approaches. It also provides a detailed appraisal of the
datasets and evaluation metrics in the field, as well as the subtask of Pronoun
Resolution that has seen various improvements in the recent years. We highlight
the advantages and disadvantages of the approaches, the challenges of the task,
the lack of agreed-upon standards in the task and propose a way to further
expand the boundaries of the field.
