| 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 . |
| Outcome: | The encoder-based model outperforms the decoder--based models in accuracy and speed . older model generalizes the best to out-of-domain textual genres . |
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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. |
| Outcome: | The proposed model outperforms the leading multilingual CR model by 2 percentage points across all languages in the CorefUD v1.2 dataset. |
Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective (2024.findings-acl)
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| Challenge: | a recent study shows that evaluations of CR models on multiple datasets conflate different factors concerning what is being measured. |
| Approach: | They propose to view evaluations through the lens of measurement modeling . they show that evaluations risk conflating different factors concerning what is being measured . |
| Outcome: | The evaluations on seven datasets show that models that reflect coreference generalization are often correlated with differences in how coreference is defined and operationalized. |
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. |
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. |
Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood. |
| Approach: | They propose that future efforts should improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
| Outcome: | The proposed methods improve scope, data, and evaluation methods of traditional coreference research to adapt to the development of LLMs. |
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. |
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 . |
| Approach: | They propose to leverage automatically constructed discourse parse trees within a neural approach to generate anaphoric mentions. |
| Outcome: | The proposed model improves on two benchmark entity coreference-resolution 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. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks (2020.lrec-1)
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| Challenge: | Existing word embeddings rely on local information delimited by context windows or dependency parents to predict word relations. |
| Approach: | They propose to use coreference resolution to find all spans of a text that refer to the same entity to improve the F1-Scores. |
| Outcome: | The proposed methods do not benefit significantly from pronoun substitution. |
End-to-End Neural Bridging Resolution (2022.coling-1)
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| Challenge: | state-of-the-art resolvers for bridging resolution are weaker than entity coreference resolution. |
| Approach: | They evaluate bridging resolvers in an end-to-end setting and strengthen them with better encoders . they also try to gain a better understanding of them through perturbation experiments . |
| Outcome: | bridging resolvers are evaluated in an end-to-end setting and strengthened with better encoders . bribridging resolution is the task of identifying briating anaphors and linking them to their antecedents - a paper by the journal bribing resolution argues . |