# Solving Hard Coreference Problems

arXiv: https://arxiv.org/abs/1907.05524 · HTML: https://mlsanity.com/paper/1907.05524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

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

- [Learning from Noisy Labels for Entity-Centric Information Extraction](https://mlsanity.com/paper/2104.08656/) (2021)
- [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://mlsanity.com/paper/2104.08161/) (2021)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://mlsanity.com/paper/2303.09092/) (2023)
- [Weakly Supervised Video Scene Graph Generation via Natural Language Supervision](https://mlsanity.com/paper/2502.15370/) (2025)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [Attention-based Contrastive Learning for Winograd Schemas](https://mlsanity.com/paper/2109.05108/) (2021)
- [Actor Identification in Discourse: A Challenge for LLMs?](https://mlsanity.com/paper/2402.00620/) (2024)
- [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)
- [Experience and Prediction: A Metric of Hardness for a Novel Litmus Test](https://mlsanity.com/paper/2309.02534/) (2023)

## Abstract (arXiv)

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper, we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the state-of-the-art level on standard coreference resolution datasets.