Event mentions in text correspond to real-world events of varying degrees of
granularity. The task of subevent detection aims to resolve this granularity
issue, recognizing the membership of multi-granular events in event complexes.
Since knowing the span of descriptive contexts of event complexes helps infer
the membership of events, we propose the task of event-based text segmentation
(EventSeg) as an auxiliary task to improve the learning for subevent detection.
To bridge the two tasks together, we propose an approach to learning and
enforcing constraints that capture dependencies between subevent detection and
EventSeg prediction, as well as guiding the model to make globally consistent
inference. Specifically, we adopt Rectifier Networks for constraint learning
and then convert the learned constraints to a regularization term in the loss
function of the neural model. Experimental results show that the proposed
method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for
subevent detection, HiEve and IC, respectively, while achieving a decent
performance on EventSeg prediction.

提出了将事件分段作为辅助任务以改善子事件检测学习的方法，并采用整流网络进行约束学习，并将学习到的约束转换为神经模型的损失函数中的正则化项，实验结果表明，该方法在子事件检测中比基准方法分别提高了 2.3％和 2.5％的性能，同时在事件分割预测方面表现良好。