Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends (2024.acl-long)
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| Challenge: | Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. |
| Approach: | They propose a pipeline that trains a state-of-the-art Coreference Resolution system within the constraints of an academic budget and trains with up to 0.006x the memory resources. |
| Outcome: | The proposed framework outperforms encoder-based discriminative systems on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining 170x faster inference compared to previous state-of-the-art systems. |
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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. |
| Outcome: | The proposed model outperforms the leading multilingual CR model by 2 percentage points across all languages in the CorefUD v1.2 dataset. |
Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)
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| Challenge: | Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations. |
| Approach: | They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution . |
| Outcome: | The proposed approach achieves better performance on the English OntoNotes development set. |
A Controlled Reevaluation of Coreference Resolution Models (2024.lrec-main)
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| Challenge: | a pretrained language model is used in state-of-the-art coreference resolution models. |
| Approach: | They evaluate five coreference resolution models and control for language model used . they find that encoder-based CR models outperform decoder--based models in accuracy . |
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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. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
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 through a seq2seq Transition-Based System (2023.tacl-1)
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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. |
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)
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| Challenge: | despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved. |
| Approach: | They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved. |
| Outcome: | The proposed model improves the performance of entity coreference resolvers. |
Fast End-to-end Coreference Resolution for Korean (2020.findings-emnlp)
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| Challenge: | Recent advances in coreference resolution have come at a cost of computational complexity and have not been addressed. |
| Approach: | They propose a pointer network that leverages the linguistic property of head-final languages to reduce coreference linking search space and achieve 2x speedup in document processing time. |
| Outcome: | The proposed model maintains state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time. |
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. |
Seq2seq is All You Need for Coreference Resolution (2023.emnlp-main)
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| Challenge: | Existing work on coreference resolution suggests task-specific models are necessary . a recent line of work that take an alternative approach leveraging advances in seq2seq-based models is needed . |
| Approach: | They propose a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation. |
| Outcome: | The proposed model outperforms or matches the best coreference systems on an array of datasets. |