A Neural Entity Coreference Resolution Review

TL;DR

This paper reviews recent neural network-based methods for entity coreference resolution, discussing datasets, metrics, challenges, and recent improvements, aiming to guide future research in this complex NLP task.

Contribution

It provides a comprehensive overview of current neural approaches, evaluates datasets and metrics, and discusses challenges and future directions in coreference resolution.

Findings

Neural methods have advanced coreference resolution accuracy.

Pronoun resolution has seen notable recent improvements.

Challenges include lack of standardization and dataset biases.

Abstract

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.