Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

Document-level Event Causality Identification (DECI) aims to identify causal relations
between event pairs in a document. It poses a great challenge of across-sentence reasoning …

To understand event structures of documents, event causality identification (ECI) emerges
as a crucial task, aiming to discern causal relationships among event mentions. The latest …

Abstract Causality Explanation Generation refers to generate an explanation in natural
language given an initial cause-effect pair. It demands rigorous explicit rationales to …

Temporal relation extraction aims to extract temporal relations between event pairs, which is
crucial for natural language understanding. Few efforts have been devoted to capturing the …

Abstract Document-level Event Causality Identification (DECI) aims to recognize causal
relations between events within a document. Recent studies focus on building a document …

Abstract Event Causality Extraction (ECE) plays an essential role in many Natural Language
Processing (NLP), such as event prediction and dialogue generation. Recent research in …

Event Causality Identification (ECI) refers to the detection of causal relations between events
in texts. However, most existing studies focus on sentence-level ECI with high-resource …