Review of coreference resolution in English and Persian

TL;DR

This paper reviews recent advancements in coreference resolution for English and Persian, analyzing datasets, evaluation metrics, methodologies, and the unique challenges faced in Persian CR, highlighting future research directions.

Contribution

It provides a comprehensive overview of CR techniques, datasets, and evaluation methods, with a special focus on Persian language challenges and the application of neural models like ParsBERT.

Findings

Deep learning models outperform rule-based approaches.

Persian CR faces unique resource and linguistic challenges.

End-to-end neural models show promising results in Persian.

Abstract

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.