| 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. |
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Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
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| Challenge: | Existing systems for coreference resolution are difficult because of their long coreferent chains. |
| Approach: | They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system . |
| Outcome: | The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task. |
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. |
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)
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| Challenge: | Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity) |
| Approach: | They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it. |
| Outcome: | The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve . |
Coreference Resolution with Entity Equalization (P19-1)
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| Challenge: | Existing approaches to coreference resolution capture the properties of entity clusters and use them in the resolution process. |
| Approach: | They propose an approach that captures entities and uses them in coreference resolution . they propose an "Entity Equalization" mechanism that represents each mention in a cluster . |
| Outcome: | The proposed approach improves the CoNLL-2012 coreference resolution task by 3.6%. |
Cross-document Coreference Resolution over Predicted Mentions (2021.findings-acl)
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| Challenge: | Cross-document coreference resolution has been under-explored in recent years . however, the challenge of cross-document resolution remains relatively under-studied . |
| Approach: | They propose a model for cross-document coreference resolution from raw text that extends a prominent withindocument corefer model to the CD setting. |
| Outcome: | The proposed model achieves competitive results for event and entity coreference resolution on gold mentions. |
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. |
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. |
Global Entity Disambiguation with BERT (2022.naacl-main)
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| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)
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| Challenge: | Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself. |
| Approach: | They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces. |
| Outcome: | The proposed version outperforms baseline models on unsupervised question answering, supervised relation classification and entity linking tasks. |
A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
| Outcome: | The proposed model is based on the Transformer-based model with state-of-the-art results . the proposed model has little cognitive motivation and is too small to perform ablation studies . |