# Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning

arXiv: https://arxiv.org/abs/2212.10087 · HTML: https://mlsanity.com/paper/2212.10087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [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)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [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)
- [A Neural Transition-based Model for Nested Mention Recognition](https://mlsanity.com/paper/1810.01808/) (2018)
- [An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction](https://mlsanity.com/paper/2106.14611/) (2021)
- [A Coarse to Fine Question Answering System based on Reinforcement Learning](https://mlsanity.com/paper/2106.00257/) (2021)

## Built on by (most cited first, 1 of 1)

- [End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems](https://mlsanity.com/paper/2504.05824/) (2025)

## Abstract (arXiv)

A coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to tackle two main tasks: one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this paper, we propose a hybrid rule-neural coreference resolution system based on actor-critic learning, such that it can achieve better coreference performance by leveraging the advantages from both the heuristic rules and a neural conference model. This end-to-end system can also perform both mention detection and resolution by leveraging a joint training algorithm. We experiment on the BERT model to generate input span representations. Our model with the BERT span representation achieves the state-of-the-art performance among the models on the CoNLL-2012 Shared Task English Test Set.