Cross-document Event Coreference Search: Task, Dataset and Modeling
Alon Eirew, Avi Caciularu, Ido Dagan

TL;DR
This paper introduces the task of cross-document event coreference search, creates a dataset from Wikipedia, and proposes a novel model that improves coreference retrieval performance by integrating coreference scoring into a passage retrieval framework.
Contribution
It defines the new task of cross-document event coreference search, provides a dataset derived from Wikipedia, and develops a novel model that enhances coreference scoring within a retrieval architecture.
Findings
The proposed model outperforms baseline retrieval methods.
The dataset enables research on large-scale event coreference search.
Integrating coreference scoring improves retrieval accuracy.
Abstract
The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task - Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreference dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting,…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Graph Neural Networks
