| Challenge: | Entity coreference resolution aims to identify mentions that refer to the same entity. |
| Approach: | They propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. |
| Outcome: | The proposed system generates affinity scores between mentions for coreference resolution. |
Similar Papers
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (P18-2)
Copied to clipboard
| Challenge: | Existing methods for identifying and clustering mentions in text are complex and require heuristics to solve. |
| Approach: | They propose to use a biaffine attention model to get antecedent scores for each possible mention and optimize mention detection and mention clustering accuracy given the mention cluster labels. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set. |
Coreference Resolution through a seq2seq Transition-Based System (2023.tacl-1)
Copied to clipboard
| Challenge: | Recent coreference resolution systems use search algorithms to identify mentions and resolve coreference. |
| Approach: | They propose a text-to-text coreference resolution system that uses a semantic paradigm to predict mentions and links jointly. |
| Outcome: | The proposed system achieves state-of-the-art accuracy on CoNLL-2012 datasets with 83.3 F1-score for English, 68.5 F1 score for Arabic, and 74.3 F1 scores for Chinese. |
Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
Copied to clipboard
| Challenge: | Current neural coreference models are trained on monolingual annotated data but annotating such coreference information is expensive and challenging. |
| Approach: | They propose a simple yet effective model to exploit coreference knowledge from parallel data. |
| Outcome: | The proposed model improves on the OntoNotes 5.0 English dataset by 1.74 percentage points . it is based on an unsupervised module learning coreference from annotations . |
Pre-training Mention Representations in Coreference Models (2020.emnlp-main)
Copied to clipboard
| 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. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
Copied to clipboard
| 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. |
Neural Models for Reasoning over Multiple Mentions Using Coreference (N18-2)
Copied to clipboard
| 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. |
Sanaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution (L18-1)
Copied to clipboard
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 . |
| Approach: | They propose to improve coreference resolution by adding semantic features to a top-level deep neural network system . they evaluate a shared task dataset and compare it to the state-of-the-art system based on Stanford deep-coref . |
| Outcome: | The proposed system achieves 1.13% gain over the CoNLL 2012 dataset and the state-of-the-art system. |
LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution (2023.eacl-main)
Copied to clipboard
| 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. |
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking (C18-1)
Copied to clipboard
| 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. |
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)
Copied to clipboard
| 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 . |