Papers with event coreference model

3 papers
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data (N19-1)

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Challenge: Argument compatibility is a linguistic condition that is often used in event coreference resolution systems.
Approach: They propose a transfer learning framework that uses unlabeled data to learn argument compatibility of event mentions.
Outcome: The proposed model improves the performance of the overall event coreference model on the English dataset.
What happens before and after: Multi-Event Commonsense in Event Coreference Resolution (2023.eacl-main)

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Challenge: Existing event coreference models cluster event mentions pertaining to the same event, but they fail to leverage commonsense inferences for lexically-divergent mentions.
Approach: They propose a model that extends event mentions with temporal commonsense inferences to generate plausible events that happen before and after the target events.
Outcome: The proposed model generates plausible events that happen before and after the target event, and then after it, such as "he was sentenced".
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.

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