To mitigate forgetting, existing lifelong event detection methods typically maintain a memory module and replay the stored memory data during the learning of a new task. However, the simple combination of memory data and new-task samples can still result in substantial forgetting of previously acquired knowledge, which may occur due to the potential overlap between the feature distribution of new data and the previously learned embedding space. Moreover, the model suffers from overfitting on the few memory samples rather than effectively remembering learned patterns. To address the challenges of forgetting and overfitting, we propose a novel method based on embedding space separation and compaction. Our method alleviates forgetting of previously learned tasks by forcing the feature distribution of new data away from the previous embedding space. It also mitigates overfitting by a memory calibration mechanism that encourages memory data to be close to its prototype to enhance intra-class compactness. In addition, the learnable parameters of the new task are initialized by drawing upon acquired knowledge from the previously learned task to facilitate forward knowledge transfer. With extensive experiments, we demonstrate that our method can significantly outperform previous state-of-the-art approaches.

为了解决遗忘和过拟合问题，我们提出了一种基于嵌入空间分离和压缩的新方法。该方法通过迫使新数据的特征分布与先前的嵌入空间分离来减轻先前学习任务的遗忘，并通过内类紧凑性增强机制鼓励记忆数据靠近其原型，从而减轻过拟合。此外，新任务的学习参数通过利用先前学习任务中获得的知识来初始化，以促进前向知识传输。通过大量实验证明，我们的方法可以显著优于先前的最先进方法。

嵌入空间分离与压缩的终身事件检测