# Sequential Cross-Document Coreference Resolution

arXiv: https://arxiv.org/abs/2104.08413 · HTML: https://mlsanity.com/paper/2104.08413/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://mlsanity.com/paper/1710.06481/) (2017)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://mlsanity.com/paper/2010.02807/) (2020)
- [Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization](https://mlsanity.com/paper/1805.10985/) (2018)
- [Learning Global Features for Coreference Resolution](https://mlsanity.com/paper/1604.03035/) (2016)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://mlsanity.com/paper/1707.07344/) (2017)
- [CDLM: Cross-Document Language Modeling](https://mlsanity.com/paper/2101.00406/) (2021)
- [Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling](https://mlsanity.com/paper/2009.11032/) (2020)
- [Paraphrasing vs Coreferring: Two Sides of the Same Coin](https://mlsanity.com/paper/2004.14979/) (2020)
- [Resource-Enhanced Neural Model for Event Argument Extraction](https://mlsanity.com/paper/2010.03022/) (2020)
- [A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution](https://mlsanity.com/paper/1504.05929/) (2015)
- [A Cluster Ranking Model for Full Anaphora Resolution](https://mlsanity.com/paper/1911.09532/) (2019)
- [A Deep Cascade Model for Multi-Document Reading Comprehension](https://mlsanity.com/paper/1811.11374/) (2018)
- [Multilevel Text Alignment with Cross-Document Attention](https://mlsanity.com/paper/2010.01263/) (2020)
- [Recursive Subtree Composition in LSTM-Based Dependency Parsing](https://mlsanity.com/paper/1902.09781/) (2019)

## Built on by (most cited first, 12 of 12)

- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://mlsanity.com/paper/2210.12654/) (2022)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://mlsanity.com/paper/2302.09715/) (2023)
- [Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities](https://mlsanity.com/paper/2205.11438/) (2022)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG](https://mlsanity.com/paper/2410.11494/) (2024)
- [Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks](https://mlsanity.com/paper/2506.12925/) (2025)
- [Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://mlsanity.com/paper/2406.02148/) (2024)
- [Sentence-Incremental Neural Coreference Resolution](https://mlsanity.com/paper/2305.16947/) (2023)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)
- ['If you build they will come': Automatic Identification of News-Stakeholders to detect Party Preference in News Coverage](https://mlsanity.com/paper/2212.08864/) (2022)
- [Efficient Seq2seq Coreference Resolution Using Entity Representations](https://mlsanity.com/paper/2510.14504/) (2025)

## Abstract (arXiv)

Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while provides strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.