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. |
Similar Papers
Neural Mention Detection (2020.lrec-1)
Copied to clipboard
| Challenge: | Mention detection is an important preprocessing step for downstream applications such as NER and coreference resolution. |
| Approach: | They propose and compare three approaches to mention detection using ELMO embeddings and a biaffine classifier. |
| Outcome: | The proposed model outperforms state-of-the-art models on the GENIA corpora and improves on mention recall. |
Active Learning for Coreference Resolution using Discrete Annotation (2020.acl-main)
Copied to clipboard
| Challenge: | Exhaustively annotating coreference is expensive as it requires tracking coreference chains across long passages of text. |
| Approach: | They propose a pairwise annotation technique which asks annotators to identify mention antecedents if a presented mention pair is not coreferent. |
| Outcome: | The proposed method is much more efficient when combined with a mention clustering algorithm for selecting which examples to label . future work can use the proposed protocol to develop coreference models for new domains. |
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. |
Triad-based Neural Network for Coreference Resolution (C18-1)
Copied to clipboard
| 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. |
Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)
Copied to clipboard
| Challenge: | Recent results show that annotating mentions is twice as fast as annotation of full coreference chains. |
| Approach: | They propose a method for efficiently adapting coreference models using only mentions in the target domain without increasing annotator time. |
| Outcome: | The proposed method improves average F1 without increasing annotator time. |
End-to-end Deep Reinforcement Learning Based Coreference Resolution (P19-1)
Copied to clipboard
| Challenge: | Recent neural network models for coreference resolution are usually trained with heuristic loss functions that are computed over a sequence of local decisions. |
| Approach: | They propose an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. |
| Outcome: | The proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark. |
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
Copied to clipboard
| 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. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing attempts to integrate singleton mention detection into end-to-end coreference resolution for English have been hampered by the lack of singletont mention spans in the OntoNotes benchmark. |
| Approach: | They propose a two-step neural mention and coreference resolution system that integrates singleton mentions with OntoNotes syntax trees to achieve a near approximation of the Ontonotes dataset with all singletont mentions. |
| Outcome: | The proposed system achieves 94% recall on a sample of gold singletons. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
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. |