Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Document-level event extraction aims to recognize event information from a whole piece of
article. Existing methods are not effective due to two challenges of this task: a) the target …

Event Detection (ED) aims to recognize mentions of events (ie, event triggers) and their
types in text. Recently, several ED datasets in various domains have been proposed …

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 …

Event Detection (ED) aims to identify event trigger words from a given text and classify it into
an event type. Most of current methods to ED rely heavily on training instances, and almost …

We consider the problem of collectively detecting multiple events, particularly in cross-
sentence settings. The key to dealing with the problem is to encode semantic information …

Event argument extraction is an essential task in event extraction, and become particularly
challenging in the case of low-resource scenarios. We solve the issues in existing studies …

Event detection (ED) is a critical subtask of event extraction that seeks to identify event
triggers of certain types in texts. Despite significant advances in ED, existing methods …

We address the sampling bias and outlier issues in few-shot learning for event detection, a
subtask of information extraction. We propose to model the relations between training tasks …

Event detection (ED) aims at detecting event trigger words in sentences and classifying them
into specific event types. In real-world applications, ED typically does not have sufficient …