Event extraction (EE) is an essential task of information extraction, which
aims to extract structured event information from unstructured text. Most prior
work focuses on extracting flat events while neglecting overlapped or nested
ones. A few models for overlapped and nested EE includes several successive
stages to extract event triggers and arguments,which suffer from error
propagation. Therefore, we design a simple yet effective tagging scheme and
model to formulate EE as word-word relation recognition, called OneEE. The
relations between trigger or argument words are simultaneously recognized in
one stage with parallel grid tagging, thus yielding a very fast event
extraction speed. The model is equipped with an adaptive event fusion module to
generate event-aware representations and a distance-aware predictor to
integrate relative distance information for word-word relation recognition,
which are empirically demonstrated to be effective mechanisms. Experiments on 3
overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show
that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the
inference speed of OneEE is faster than those of baselines in the same
condition, and can be further substantially improved since it supports parallel
inference.

OneEE 是一种简单而有效的标注方案和模型，将事件提取作为词词关系识别来表述，它在同时识别触发词或参数词之间的关系方面表现出色，具有很快的事件提取速度，并在 FewFC、Genia11 和 Genia13 三个基准测试上取得了最先进的结果。