Lifelong event detection aims to incrementally update a model with new event types and data while retaining the capability on previously learned old types. One critical challenge is that the model would catastrophically forget old types when continually trained on new data. In this paper, we introduce Episodic Memory Prompts (EMP) to explicitly preserve the learned task-specific knowledge. Our method adopts continuous prompt for each task and they are optimized to instruct the model prediction and learn event-specific representation. The EMPs learned in previous tasks are carried along with the model in subsequent tasks, and can serve as a memory module that keeps the old knowledge and transferring to new tasks. Experiment results demonstrate the effectiveness of our method. Furthermore, we also conduct a comprehensive analysis of the new and old event types in lifelong learning.
翻译:终身事件探测旨在逐步更新一个带有新事件类型和数据的模式,同时保留以往学到的旧类型的能力,一个关键的挑战就是,当不断接受新数据培训时,该模式将灾难性地忘记旧类型。在本文件中,我们引入了“记忆提示”以明确保存所学到的特定任务知识。我们的方法对每一项任务都采用连续的快速方法,并且优化了这些方法以指导模型预测和学习特定事件的代表性。在以往工作中学到的EMP在以后的任务中与模型一起运行,并可以作为一个记忆模块,保持旧知识,向新任务转移。实验结果证明了我们的方法的有效性。此外,我们还对终身学习中新老事件类型进行了全面分析。