Cross-document Event Coreference Search: Task, Dataset and Modeling (2022)

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 Coreferecene 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, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.

## introduction cross-document event coreference (cdec) resolution is the task of identifying clusters of text mentions, across multiple texts, that refer to the same event. for example, consider the following two underlined event mentions from the wec-eng cdec dataset @xcite : 1. ...on 14 april 2010, an earthquake struck the prefecture, registering a magnitude of 6.9 (usgs, emsc) or 7.1 (xinhua). it originated in the yushu tibetan autonomous prefecture... 2. ...a school mostly for tibetan orphans in chindu county, qinghai, after the 2010 yushu earthquake destroyed the old school... both event mentions refer to the same earthquake, as can be determined by the shared event arguments @xcite . in event coreference resolution, the goal is to cluster event mentions that refer to the same event, whether within a single document or across a document collection. currently, with the growing number of documents describing real-world events and eventoriented information, the need for efficient methods for accessing such information is apparent. successful and efficient identification, clustering, and access to event-related information, may be beneficial for a broad range of applications at the multi-text level, that need to match and integrate information across documents, such as multidocument summarization @xcite , multi-hop question answering @xcite and knowledge base population (kbp) @xcite . currently, the cdec task, as formed in corresponding datasets, is intended at creating models that exhaustively resolve all coreference links in a given dataset. however, an applicable realistic scenario may require to efficiently search and extract coreferring events of only specific events of interest. a typical such use-case can be of a user reading a text and encountering an event of interest (for example, the plane crash event in figure 1 ), and then wishing to further explore and learn about the event from a large document collection. to address such needs, we propose an appealing, and often more applicable, complementary set up for the task -cross-document coreference search (figure 1 ), 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 corpus. such coreference resolution search use-case cannot be addressed currently, for two main reasons: (1) existing cdec datasets are relatively small for the realistic representation of a search task; (2) current cdec models, which are designed at linking all coreference links in a given dataset, are inapplicable in terms of computation at the much larger search space required by realistic coreference resolution search scenarios. to facilitate research on this setup, we present a large dataset, derived from wikipedia, by leveraging existing annotations in the wikipedia event coreference dataset (wec) @xcite . our curated dataset resembles in structure to an open-domain qa (odqa) dataset @xcite @xcite @xcite , containing a set of coreference queries and a large passage collection for retrieval. 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, as an appealing baseline. further, motivated to integrate coreference modeling into dpr, we adapted components inspired by a prominent within-document end-to-end coreference resolution model @xcite , which was previously applied also to the cdec task @xcite . thus, we developed an integrated model that leverages components from both dpr and the coreference model of @xcite . our novel model yields substantially improved performance on several important evaluation metrics. our dataset foot_0 and code foot_1 are released for open access. ## background in this section, we first describe the cross document event coreference (cdec) task, datasets and models ( §2.1) and then review the common open-domain qa model architecture ( §2.2). ## the coresearch dataset we formulated the cross-document event coreference search task following a similar approach to open-domain question answering (illustrated in figure 1 ). specifically, given a query containing a marked target event mention, along with a passage collection, the goal is to retrieve all the passages from the passage collection that contain an event mention coreferring with the query event, and extract the coreferring mention span of each retrieved passage. to facilitate research on this task, we present a large dataset, derived from wikipedia, termed core-search. in this section we describe the coresearch dataset structure ( §3.1), following by describing the structure of a single query instance ( §3.2). ## coreference-search models in this section, we aim to devise an effective baseline for our event coreference search task to be trained on our dataset. following the observation that coreference search formulation resembles the open-domain qa (odqa) ( §2.2), we propose an end-to-end neural architecture, comprised of a retriever and a reader models. given a query passage, the retriever selects the top-k most relevant passage candidates out of the entire passage corpus ( §4.1). then, the reader is responsible for re-ranking the retrieved passages and extracting the coreferring event span, by using a reading comprehension module ( §4.2). ## conclusions we introduced cross-document coreference search, a challenging task for accurate semantic search for events. to support research on this task, we created the wikipedia-based coresearch dataset, comprised of training, validation, and test set queries, along with a large collection of about 1m passages to retrieve from in each set. furthermore, our methodology for semiautomatically converting a cross-document event coreference dataset to a coreference search dataset can be applied to other such datasets, for example hypercoref @xcite which represents the news domain. finally, we provide several effective baseline models and encourage future research on this promising and practically applicable task, hoping that it will lead to a broad set of novel applications and use-cases.