| 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. |
Similar Papers
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| 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. |
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
| Outcome: | The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus. |
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. |
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering (P18-2)
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| 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. |
Triad-based Neural Network for Coreference Resolution (C18-1)
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| 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. |
Parallel Data Helps Neural Entity Coreference Resolution (2023.findings-acl)
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| 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 . |
Coreference Reasoning in Machine Reading Comprehension (2021.acl-long)
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| Challenge: | Existing datasets for machine reading comprehension do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Approach: | They propose to use existing coreference resolution datasets to train machine reading comprehension models to better reflect the natural distribution and, consequently, the challenges of coreference reasoning. |
| Outcome: | The proposed method improves the performance of state-of-the-art models on a set of coreference-related datasets. |
Tracing Origins: Coreference-aware Machine Reading Comprehension (2022.acl-long)
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| Challenge: | a recent study has enriched pre-trained language models with syntactic, semantic and other linguistic information to improve their performance. |
| Approach: | They use a pre-trained language model to leverage coreference information to enhance word embeddings . they use additional encoder layers to focus on coreference mentions or a relational graph convolutional network to model the coreference relations. |
| Outcome: | The proposed model imitates the human reading process and leverages coreference information to enhance word embeddings. |
Neural Mention Detection (2020.lrec-1)
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| 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. |
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024.lrec-main)
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| 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. |