The Referential Reader: A Recurrent Entity Network for Anaphora Resolution (P19-1)
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| Challenge: | Existing methods for storing and accessing entity mentions are expensive and implausible for human readers. |
| Approach: | They propose a method for storing and accessing entity mentions during online text processing. |
| Outcome: | The proposed model performs well on a dataset of pronoun-name anaphora. |
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Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)
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| Challenge: | bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution. |
| Approach: | This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Outcome: | The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols. |
Neural Models for Reasoning over Multiple Mentions Using Coreference (N18-2)
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| Challenge: | Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. |
| Approach: | They propose a recurrent layer which is instead biased towards coreferent dependencies and uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. |
| Outcome: | The proposed layer improves performance on Wikihop, LAMBADA and the bAbi AI datasets with large gains when training data is scarce. |
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking (C18-1)
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| Challenge: | Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. |
| Approach: | They propose a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions and a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning. |
| Outcome: | The proposed model outperforms existing models designed for singular mentions and plural mentions. |
Named Entity Recognition With Parallel Recurrent Neural Networks (P18-2)
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| Challenge: | Named entity recognition is an important element of natural language understanding . a shift in focus has been on designing better neural architectures for solving NER . |
| Approach: | They propose a new architecture for named entity recognition that uses multiple LSTM units instead of a single LStm component. |
| Outcome: | The proposed architecture achieves state-of-the-art on the CoNLL 2003 NER dataset . |
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks (2020.emnlp-main)
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| Challenge: | Current models for document coreference resolution have large memory requirements and quadratic runtime in document length. |
| Approach: | They propose a memory-augmented neural network that tracks only a small number of entities at a time. |
| Outcome: | The proposed model outperforms existing models on OntoNotes and LitBank in memory management and memory management. |
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. |
ISR: Self-Refining Referring Expressions for Entity Grounding (2025.acl-long)
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| Challenge: | Entity grounding is a crucial task in the construction of multimodal knowledge graphs. |
| Approach: | They propose a novel scheme to enhance the multimodal large language model's capability to generate high quality REs for the given entities as explicit contextual clues. |
| Outcome: | The proposed method surpasses other methods in entity grounding, highlighting its effectiveness, robustness and potential for broader applications. |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
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| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Outcome: | The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable. |
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |