LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution (2023.eacl-main)
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| Challenge: | Current coreference systems use a single pairwise scoring component to assign mentions a score . different kinds of mentions require different information sources to assess their score - a problem that requires many decisions . |
| Approach: | They propose a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated scoring function for each category. |
| Outcome: | The proposed model significantly improves the pairwise scorer and overall performance on the English Ontonotes coreference corpus and 5 additional datasets. |
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| Challenge: | Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference. |
| Approach: | They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly. |
| Outcome: | The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese. |
Interpretable Coreference Resolution Evaluation Using Explicit Semantics (2026.acl-long)
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| Challenge: | Existing evaluation methods for coreference resolution are limited by semantic and contextual information. |
| Approach: | They propose a semantically-enhanced evaluation framework for coreference resolution that assigns semantic labels to nominal mentions and propagates them to entire coreference clusters. |
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CorefInst: Leveraging LLMs for Multilingual Coreference Resolution (2026.tacl-1)
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| Challenge: | Existing methods for CR are encoder-only, decoder-based and asynchronous models. |
| Approach: | They propose a multilingual CR methodology which leverages decoder-only LLMs to handle overt and zero mentions. |
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Active Learning for Coreference Resolution using Discrete Annotation (2020.acl-main)
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| Challenge: | Exhaustively annotating coreference is expensive as it requires tracking coreference chains across long passages of text. |
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IdentifyMe: A Challenging Long-Context Mention Resolution Benchmark for LLMs (2025.naacl-short)
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| Challenge: | Recent evaluations of LLMs on coreference resolution have revealed that traditional output formats and evaluation metrics do not fully capture the models’ referential understanding. |
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BERT for Coreference Resolution: Baselines and Analysis (D19-1)
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| Challenge: | Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition. |
| Approach: | They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks. |
| Outcome: | A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing. |
Triad-based Neural Network for Coreference Resolution (C18-1)
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| Challenge: | Entity coreference resolution aims to identify mentions that refer to the same entity. |
| Approach: | They propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. |
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Investigating Multilingual Coreference Resolution by Universal Annotations (2023.findings-emnlp)
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| Challenge: | Existing systems for multilingual coreference resolution have been challenging due to linguistic diversity and complexity of different languages. |
| Approach: | They propose a multilingual coreference dataset with universal morphosyntactic and coreference annotations. |
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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. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on 6 out of 7 datasets. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
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