# Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes

arXiv: https://arxiv.org/abs/2112.09742 · HTML: https://mlsanity.com/paper/2112.09742/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [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)
- [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://mlsanity.com/paper/1805.04893/) (2018)
- [WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://mlsanity.com/paper/2109.07449/) (2021)
- [AD3: Attentive Deep Document Dater](https://mlsanity.com/paper/1902.02161/) (2019)
- [Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://mlsanity.com/paper/2110.05727/) (2021)

## Built on by (most cited first, 4 of 4)

- [Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution](https://mlsanity.com/paper/2210.07602/) (2022)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://mlsanity.com/paper/2303.09092/) (2023)
- [Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution](https://mlsanity.com/paper/2410.09448/) (2024)
- [What makes an entity salient in discourse?](https://mlsanity.com/paper/2508.16464/) (2025)

## Abstract (arXiv)

Current work on automatic coreference resolution has focused on the OntoNotes benchmark dataset, due to both its size and consistency. However many aspects of the OntoNotes annotation scheme are not well understood by NLP practitioners, including the treatment of generic NPs, noun modifiers, indefinite anaphora, predication and more. These often lead to counterintuitive claims, results and system behaviors. This opinion piece aims to highlight some of the problems with the OntoNotes rendition of coreference, and to propose a way forward relying on three principles: 1. a focus on semantics, not morphosyntax; 2. cross-linguistic generalizability; and 3. a separation of identity and scope, which can resolve old problems involving temporal and modal domain consistency.