Active Learning for Coreference Resolution using Discrete Annotation (2020.acl-main)
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| 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. |
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| Challenge: | Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. |
| Approach: | They investigate how to actively label coreference by sampling a small subset of data for annotators to label. |
| Outcome: | The proposed model can be more realistic when labeling spans within the same document than when annotating spans across documents. |
Annotating Mentions Alone Enables Efficient Domain Adaptation for Coreference Resolution (2023.acl-long)
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| Challenge: | Recent results show that annotating mentions is twice as fast as annotation of full coreference chains. |
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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. |
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Pre-training Mention Representations in Coreference Models (2020.emnlp-main)
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| Challenge: | Existing methods to improve coreference resolution use labeled data. |
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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. |
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2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)
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| Challenge: | Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult to learn coreference beyond surface matching. |
| Approach: | They propose a heuristic to efficiently filter out a large number of non-coreferent pairs and a training approach on a balanced set of coreferent and non- coreferente mention pairs. |
| Outcome: | The proposed approach significantly reduces compute requirements on two popular ECR datasets while reducing the computational complexity. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| 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. |
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. |
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ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)
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Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, Brendan O’Connor
| Challenge: | Existing datasets vary in definition of coreferences and are curated for linguistic experts. |
| Approach: | They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets. |
| Outcome: | The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them. |
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. |