Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan
| Challenge: | Using Ontonotes, documents in certain genres were split into smaller parts for ease of annotation. |
| Approach: | They propose to merge annotations from documents split into smaller parts in Ontonotes for ease of annotation. |
| Outcome: | The proposed corpus restores documents to their original form, revealing dramatic increases in length in certain genres. |
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| Challenge: | Existing methods for coreference resolution are unable to evaluate generalizability to open domain data. |
| Approach: | They propose to make an OntoNotes-like coreference dataset publicly available and convert it into an English corpus. |
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Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks (2020.emnlp-main)
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| Challenge: | Current models for document coreference resolution have large memory requirements and quadratic runtime in document length. |
| Approach: | They propose a memory-augmented neural network that tracks only a small number of entities at a time. |
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xCoRe: Cross-context Coreference Resolution (2025.emnlp-main)
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| Challenge: | Current coreference resolution systems are limited to short-to-medium-sized documents and struggle to scale to very long documents due to architectural limitations and implied memory costs. |
| Approach: | They propose a unified approach to coreference resolution that unifies two challenging settings . they use a pipeline that first identifies mentions, then creates clusters within individual contexts . |
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BOOKCOREF: Coreference Resolution at Book Scale (2025.acl-long)
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| Challenge: | Existing benchmarks for coreference resolution systems are limited in length and do not adequately assess system capabilities at the book scale. |
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Incremental Neural Coreference Resolution in Constant Memory (2020.emnlp-main)
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| Challenge: | Existing work on coreference resolution has focused on improving pairwise span scoring functions and methods for decoding into globally consistent clusters. |
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Moving on from OntoNotes: Coreference Resolution Model Transfer (2021.emnlp-main)
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| Challenge: | Academic neural models for coreference resolution (coref) are typically trained on OntoNotes and model improvements are benchmarked on that dataset. |
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| Challenge: | a new ontology is based on coreference resolution, but it is not comprehensive . a recent study found that ontologies categorize concepts into groups and arrange them in a hierarchy . |
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ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference (2022.lrec-1)
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| Challenge: | Existing corpus ParCorFull contains parallel texts for English-German, French and Portuguese . translation of coreference across languages is challenging for MT and other NLP applications . |
| Approach: | They describe a parallel corpus annotated with full coreference chains for multiple languages . they use the existing corpus ParCorFull to study translation of coreference across languages - a challenge for machine translation and NLP . |
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Word-Level Coreference Resolution (2021.emnlp-main)
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| Challenge: | Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. |
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Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
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| Challenge: | Current neural coreference models are trained on monolingual annotated data but annotating such coreference information is expensive and challenging. |
| Approach: | They propose a simple yet effective model to exploit coreference knowledge from parallel data. |
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