| 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. |
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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. |
Scaling Within Document Coreference to Long Texts (2021.findings-acl)
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Raghuveer Thirukovalluru, Nicholas Monath, Kumar Shridhar, Manzil Zaheer, Mrinmaya Sachan, Andrew McCallum
| Challenge: | Existing end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. |
| Approach: | They propose an approximation to end-to-end coreference resolution models which scales gracefully to documents of any length. |
| Outcome: | The proposed model reduces training and inference time and memory costs compared to current models with minimal loss in accuracy. |
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 . |
| 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 . |
Incremental Neural Coreference Resolution in Constant Memory (2020.emnlp-main)
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| Challenge: | Existing work on coreference resolution has focused on improving pairwise span scoring functions and methods for decoding into globally consistent clusters. |
| Approach: | They extend an incremental clustering algorithm to utilize contextualized encoders and neural components to generate a high-performing model. |
| Outcome: | The proposed model reduces memory usage to constant space with only a 0.3% relative loss in F1 on OntoNotes 5.0. |
Word-Level Coreference Resolution (2021.emnlp-main)
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| Challenge: | Recent coreference resolution models rely heavily on span representations to find coreference links between word spans. |
| Approach: | They propose to consider coreference links between individual words rather than word spans and reconstruct the word span. |
| Outcome: | The proposed model outperforms existing models on the OntoNotes benchmark while being highly efficient. |
Higher-Order Coreference Resolution with Coarse-to-Fine Inference (N18-2)
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| Challenge: | a new approach to coreference resolution uses a span-ranking architecture as an attention mechanism to iteratively refine span representations. |
| Approach: | They propose a fully-differentiable approximation to higher-order inference for coreference resolution . they propose introducing a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor . |
| Outcome: | The proposed model significantly improves accuracy on the English OntoNotes benchmark while being far more computationally efficient. |
CorefQA: Coreference Resolution as Query-based Span Prediction (2020.acl-main)
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| Challenge: | Existing coreference resolution models suffer from mention proposal. |
| Approach: | They propose a query-based span prediction task that can retrieve mentions left out at the mention proposal stage. |
| Outcome: | The proposed model can retrieve mentions left out at the mention proposal stage and improve generalization capability using existing question answering datasets. |
Pre-training Mention Representations in Coreference Models (2020.emnlp-main)
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| Challenge: | Existing methods to improve coreference resolution use labeled data. |
| Approach: | They propose two self-supervised tasks that are closely related to coreference resolution to improve mention representation. |
| Outcome: | The proposed models improve mention representations by learning them on a GAP dataset. |
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 . |
SpanBERT: Improving Pre-training by Representing and Predicting Spans (2020.tacl-1)
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| Challenge: | Pre-training methods like BERT mask individual words or subword units, but many tasks involve reasoning about relationships between two or more spans of text. |
| Approach: | They propose a pre-training method that masks contiguous random spans instead of random tokens to train the span boundary representations to predict the entire content of the masked span. |
| Outcome: | The proposed method outperforms BERT and its better-tuned baselines on span selection tasks and on coreference resolution tasks. |