# CD2CR: Co-reference Resolution Across Documents and Domains

arXiv: https://arxiv.org/abs/2101.12637 · HTML: https://mlsanity.com/paper/2101.12637/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## 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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [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)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses](https://mlsanity.com/paper/1909.04028/) (2019)
- [Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity](https://mlsanity.com/paper/1909.02339/) (2019)
- [Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling](https://mlsanity.com/paper/2009.11032/) (2020)
- [Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization](https://mlsanity.com/paper/1809.04163/) (2018)
- [Active Learning for Coreference Resolution using Discrete Annotation](https://mlsanity.com/paper/2004.13671/) (2020)

## Built on by (most cited first, 9 of 9)

- [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021)
- [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://mlsanity.com/paper/2110.05362/) (2021)
- [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts](https://mlsanity.com/paper/2104.08809/) (2021)
- [Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information](https://mlsanity.com/paper/2406.15990/) (2024)
- [ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution](https://mlsanity.com/paper/2210.07188/) (2022)
- [Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning](https://mlsanity.com/paper/2409.15113/) (2024)
- [Link Prediction for Event Logs in the Process Industry](https://mlsanity.com/paper/2508.09096/) (2025)
- [ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links](https://mlsanity.com/paper/2509.01387/) (2025)
- [Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification](https://mlsanity.com/paper/2603.00621/) (2026)

## Abstract (arXiv)

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.