Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Abstract The tasks of Rich Semantic Parsing, such as Abstract Meaning Representation
(AMR), share similar goals with Information Extraction (IE) to convert natural language texts …

Event trigger detection, entity mention recognition, event argument extraction, and relation
extraction are the four important tasks in information extraction that have been performed …

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs)
by fine-tuning. However, existing pre-training methods have not involved modeling event …

We study the problem of Event Causality Identification (ECI) to detect causal relation
between event mention pairs in text. Although deep learning models have recently shown …

Event extraction is typically modeled as a multi-class classification problem where event
types and argument roles are treated as atomic symbols. These approaches are usually …

Existing works on information extraction (IE) have mainly solved the four main tasks
separately (entity mention recognition, relation extraction, event trigger detection, and …

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 extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Event argument extraction (EAE) is a sub-task of event extraction where the goal is to
identify roles of entity mentions for events in text. The current state-of-the-art approaches for …