Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution

Abstract

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to withindocument entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al.(2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.

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“…The current state of the art model by is based on Lee et al (2017 and uses a modern BERT-based (Devlin et al, 2019) architecture. Comparatively, CDCR, which involves co-reference resolution across multiple documents, has received less attention in recent years (Bagga and Baldwin, 1998;Rao et al, 2010;Dutta and Weikum, 2015;Barhom et al, 2019). Cattan et al (2020) jointly learns both entity and event co-reference tasks, achieving current state of the art performance for CDCR, and as such provides a strong baseline for experiments in CD 2 CR.…”

“…Cattan et al (2020) jointly learns both entity and event co-reference tasks, achieving current state of the art performance for CDCR, and as such provides a strong baseline for experiments in CD 2 CR. Both Cattan et al (2020) and Barhom et al (2019) models are trained and evaluated using the ECB+ corpus (Cybulska and Vossen, 2014) which contains news articles annotated with both entity and event mentions.…”

“…Recent CDCR work (Dutta and Weikum, 2015;Barhom et al, 2019;Cattan et al, 2020) has primarily focused on resolution of entity mentions across news articles. Despite differences in tone and political alignment, most news articles are relatively similar in terms of grammatical and lexical structure.…”

“…Despite differences in tone and political alignment, most news articles are relatively similar in terms of grammatical and lexical structure. Work based on modern transformer networks such as BERT (Devlin et al, 2019) and ElMo (Peters et al, 2018) have been pre-trained on large news corpora and are therefore well suited to news-based CDCR (Barhom et al, 2019).…”

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“…ECB+ contains 982 documents clustered into 43 topics, and has two evaluation settings: coreferring mentions occurring within a single document (within document) or across a document collection (cross document). For the event co-reference pipeline, we follow the joint modeling method of Barhom et al (2019) where they jointly represented entity and event mentions with various features and learned a pairwise mention/entity scorer for coreference classification. We augment their mention features with the mention's vector representations extracted from either GPT 2.0 or our zero-shot augmented GPT 2.0.…”