Higher-order Coreference Resolution with Coarse-to-fine Inference
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Abstract (arXiv)
We introduce a fully differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.
Key facts. Cited by 150 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2018.
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Citing papers per year: 2021: 39; 2022: 52; 2023: 28; 2024: 17; 2025: 10; 2026: 4.
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- 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
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- 2021Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translationcited by 62
- 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
- 2021Coreference Resolution without Span Representationscited by 45
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- 2025The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works17 Oct 2025
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- 2025Towards Transparent AI: A Survey on Explainable Language Models25 Sep 2025
- 2025CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution22 Sep 2025
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