Temporal event forecasting aims to predict what will happen next given the
observed events in history. Previous formulations of temporal event are
unstructured, atomic, or lacking full temporal information, thus largely
restricting the representation quality and forecasting ability of temporal
events. To address these limitations, we introduce a novel formulation for
Structured, Complex, and Time-complete Temporal Event (SCTc-TE). Based on this
new formulation, we develop a simple and fully automated pipeline for
constructing such SCTc-TEs from a large amount of news articles. Furthermore,
we propose a novel model that leverages both Local and Global contexts for
SCTc-TE forecasting, named LoGo. To evaluate our model, we construct two
large-scale datasets named MidEast-TE and GDELT-TE. Extensive evaluations
demonstrate the advantages of our datasets in multiple aspects, while
experimental results justify the effectiveness of our forecasting model LoGo.
We release the code and dataset via
this https URL

基于新的结果模型，该研究提出了 SCTc-TE 的简单且全自动的构建方法，并在此基础上开发了一种名为 LoGo 的新型模型，用于 SCTc-TE 的预测，通过构建两个大规模数据集以及广泛的评估实验证明了其优越性。