| Challenge: | Existing deep neural networks for coreference resolution for Polish have been used to resolve textual fragments that refer to the same entity in the discourse world. |
| Approach: | They propose a system combining the best deep neural architecture and sieve-based coreference resolvers ordered from most to least precise to achieve the highest results. |
| Outcome: | The proposed system improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric. |
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| Challenge: | Existing methods for identifying and clustering mentions in text are complex and require heuristics to solve. |
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| Challenge: | Existing methods to improve coreference resolution use labeled data. |
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| Challenge: | Existing systems for identifying spans as mentions are based on mention-pair formulations, but they are not generalized beyond pronouns. |
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Julien Plu, Roman Prokofyev, Alberto Tonon, Philippe Cudré-Mauroux, Djellel Eddine Difallah, Raphaël Troncy, Giuseppe Rizzo
| Challenge: | Coreference resolution is a challenging task in Natural Language Processing . since a few years, the biggest step forward has been made using deep neural networks . |
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| Challenge: | Recent years, transformer-based coreference resolution systems have achieved remarkable improvements on the CoNLL dataset. |
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| Challenge: | Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference. |
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| Challenge: | Recent advances in coreference resolution have come at a cost of computational complexity and have not been addressed. |
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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. |
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