# Review of coreference resolution in English and Persian

arXiv: https://arxiv.org/abs/2211.04428 · HTML: https://mlsanity.com/paper/2211.04428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 184 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Anaphora and Coreference Resolution: A Review](https://mlsanity.com/paper/1805.11824/) (2018)
- [PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution](https://mlsanity.com/paper/1810.09807/) (2018)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://mlsanity.com/paper/2010.02807/) (2020)
- [Syntactic Scaffolds for Semantic Structures](https://mlsanity.com/paper/1808.10485/) (2018)
- [Learning Global Features for Coreference Resolution](https://mlsanity.com/paper/1604.03035/) (2016)
- [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://mlsanity.com/paper/1805.04893/) (2018)
- [Neural Cross-Lingual Entity Linking](https://mlsanity.com/paper/1712.01813/) (2017)
- [Improving Generalization in Coreference Resolution via Adversarial Training](https://mlsanity.com/paper/1908.04728/) (2019)
- [End-to-End Neural Event Coreference Resolution](https://mlsanity.com/paper/2009.08153/) (2020)
- [Pairwise Representation Learning for Event Coreference](https://mlsanity.com/paper/2010.12808/) (2020)
- [A Neural Entity Coreference Resolution Review](https://mlsanity.com/paper/1910.09329/) (2019)
- [End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline](https://mlsanity.com/paper/2107.01700/) (2021)
- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Using Type Information to Improve Entity Coreference Resolution](https://mlsanity.com/paper/2010.05738/) (2020)
- [Joint Coreference Resolution and Character Linking for Multiparty Conversation](https://mlsanity.com/paper/2101.11204/) (2021)
- [Event Coreference Resolution for Contentious Politics Events](https://mlsanity.com/paper/2203.10123/) (2022)
- [Triad-based Neural Network for Coreference Resolution](https://mlsanity.com/paper/1809.06491/) (2018)
- [Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference](https://mlsanity.com/paper/2009.04639/) (2020)

## Abstract (arXiv)

Coreference resolution (CR), identifying expressions referring to the same real-world entity, is a fundamental challenge in natural language processing (NLP). This paper explores the latest advancements in CR, spanning coreference and anaphora resolution. We critically analyze the diverse corpora that have fueled CR research, highlighting their strengths, limitations, and suitability for various tasks. We examine the spectrum of evaluation metrics used to assess CR systems, emphasizing their advantages, disadvantages, and the need for more nuanced, task-specific metrics. Tracing the evolution of CR algorithms, we provide a detailed overview of methodologies, from rule-based approaches to cutting-edge deep learning architectures. We delve into mention-pair, entity-based, cluster-ranking, sequence-to-sequence, and graph neural network models, elucidating their theoretical foundations and performance on benchmark datasets. Recognizing the unique challenges of Persian CR, we dedicate a focused analysis to this under-resourced language. We examine existing Persian CR systems and highlight the emergence of end-to-end neural models leveraging pre-trained language models like ParsBERT. This review is an essential resource for researchers and practitioners, offering a comprehensive overview of the current state-of-the-art in CR, identifying key challenges, and charting a course for future research in this rapidly evolving field.