We aim to comprehensively identify all the event causal relations in a document, both within
a sentence and across sentences, which is important for reconstructing pivotal event …

Understanding discourse structures of news articles is vital to effectively contextualize the
occurrence of a news event. To enable computational modeling of news structures, we apply …

Event coreference resolution is an important research problem with many applications.
Despite the recent remarkable success of pretrained language models, we argue that it is …

Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of
as the communication of interesting and related events that, taken together, form a concrete …

This paper proposes a novel approach for event coreference resolution that models
correlations between event coreference chains and document topical structures through an …

Journalists usually organize and present the contents of a news article following a
welldefined structure. In this paper, we propose a novel joint model for structure-based news …

Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of
events need to be identified and clustered throughout a collection of documents. CDCR …

Document-level event factuality identification is an important subtask in event factuality and
is crucial for discourse understanding in Natural Language Processing (NLP). Previous …

Event extraction is a long-standing and challenging task in natural language processing,
and existing studies mainly focus on extracting events within sentences. However, a …

Identifying the salience (ie importance) of discourse units is an important task in language
understanding. While events play important roles in text documents, little research exists on …