PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution (D18-1)
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| Challenge: | Existing methods for coreference resolution are based on word2vec-like representations of entities. |
| Approach: | They propose a large-scale English dataset for coreference resolution . they use 38K documents and 12.5M words from English-speaking preschoolers . |
| Outcome: | The proposed dataset is more efficient with higher training-test overlap than OntoNotes . the study also shows that mention detection and clustering are more efficient on PreCo . |
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| Challenge: | Existing coreference resolution datasets are either small in scale or restrict coreference to a limited text span. |
| Approach: | They present a large-scale bilingual benchmark for long-span coreference resolution . they find that NovelCR is notably rich in long-spanning coreference pairs . |
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Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance (2021.naacl-main)
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| Challenge: | Recent work on entity coreference resolution (CR) follows current trends in Deep Learning . traditional approaches do not make use of hierarchical representations of discourse structure . |
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WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (D19-1)
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| Challenge: | Large-scale training sets for pronoun resolution are scarce, since manually labelling data is costly. |
| Approach: | They propose a language-model-based approach to solve pronoun disambiguation problems using a WikiCREM dataset. |
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Data, Data Everywhere: A Guide for Pretraining Dataset Construction (2024.emnlp-main)
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Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Bo Liu, Aastha Jhunjhunwala, Zhilin Wang, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Recent language models have impressive capabilities on a number of evaluation areas. |
| Approach: | They conduct systematic analysis of pretraining set construction to identify which methods yield the greatest gains in model accuracy. |
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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. |
| Approach: | They aim to quantify transferability of coref models based on the number of annotated documents available in the target dataset. |
| Outcome: | The proposed model improvements are consistent with the state-of-the-art results on PreCo. |
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 . |
| Outcome: | The proposed model achieves state-of-the-art results on cross-document benchmarks and strong performance on long-document data while retaining top-tier results on traditional datasets. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
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| Challenge: | Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark. |
| Approach: | They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions. |
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OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres (2021.acl-short)
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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. |
| Outcome: | The proposed dataset is the largest human-annotated coreference corpus following the OntoNotes guidelines and the first to be evaluated for consistency with the OnToNote's scheme. |
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. |
| Approach: | They propose a novel pipeline that produces high-quality coreference resolution annotations on full narrative texts and a book-scale benchmark, BOOKCOREF. |
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Coreference Resolution without Span Representations (2021.acl-short)
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| Challenge: | Pretraining has reduced many complex task-specific NLP models to simple lightweight layers. |
| Approach: | They propose a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, pruning heuristics, and more. |
| Outcome: | The proposed model performs competitively with the current standard model, while being simpler and more efficient. |