Challenge: a neural event coreference model is based on a task of determining whether two event mentions refer to the same event . event coreferent tasks require nontrivial tasks such as identifying potential arguments and linking arguments to their event mention.
Approach: They propose a neural event coreference model in which event coreference is jointly trained with five tasks.
Outcome: The proposed model achieves state-of-the-art on the KBP 2017 event coreference dataset.

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Constrained Multi-Task Learning for Bridging Resolution (2022.acl-long)

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Challenge: bridging resolution is the task of recognizing and resolving bridling anaphors in a text.
Approach: They propose a constrained multi-task learning framework for bridging resolution that exploits cross-task consistency constraints to guide the learning process and pre-train the entity coreference model on publicly available coreference data.
Outcome: The proposed model achieves state-of-the-art on three standard evaluation corpora.
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.
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Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)

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Challenge: Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge.
Approach: They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before.
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Event Coreference Resolution with Non-Local Information (2020.aacl-main)

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Challenge: Existing joint models for event coreference resolution are understudied and underexploited . current models only learn trigger detection and event coreference from annotated training data .
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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.
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Multitask Learning-Based Neural Bridging Reference Resolution (2020.coling-main)

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Challenge: Existing models for bridging references lack large corpora annotated with briding references . second challenge is different definitions of bridding used in different corpors .
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Outcome: The proposed model outperforms the best reported results on full bridging resolution by up to 8 p.p.
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.
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Cross-document Event Coreference Search: Task, Dataset and Modeling (2022.emnlp-main)

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Challenge: Cross-document Event Coreference resolution is the task of identifying clusters of text mentions that refer to the same event, whether within a single document or across a document collection.
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Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)

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Challenge: Identifying related entities and events within and across documents is fundamental to natural language understanding.
Approach: They propose an approach to entity and event coreference resolution using contrastive representation learning.
Outcome: The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others.
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023.emnlp-main)

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Challenge: Existing methods for event coreference resolution (ECR) do not leverage human-summarized rules to guide the model.
Approach: They propose to transform ECR into a cloze-style MLM task using a prompt-based approach . they introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility .
Outcome: The proposed method performs well in a state-of-the-art (SOTA) benchmark.

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