Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Event extraction requires high-quality expert human annotations, which are usually
expensive. Therefore, learning a data-efficient event extraction model that can be trained …

Event detection (ED), which means identifying event trigger words and classifying event
types, is the first and most fundamental step for extracting event knowledge from plain text …

We consider event extraction in a generative manner with template-based conditional
generation. Although there is a rising trend of casting the task of event extraction as a …

We present a study on leveraging multilingual pre-trained generative language models for
zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Event is a common but non-negligible knowledge type. How to identify events from texts,
extract their arguments, even analyze the relations between different events are important …

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 …

In this work, we explore the implicit event argument detection task, which studies event
arguments beyond sentence boundaries. The addition of cross-sentence argument …

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 …