Marked Temporal Point Processes (MTPPs) arise naturally in medical, social, commercial, and financial domains. However, existing Transformer-based methods mostly inject temporal information only via positional encodings, relying on shared or parametric decay structures, which limits their ability to capture heterogeneous and type-specific temporal effects. Inspired by this observation, we derive a novel attention operator called Hawkes Attention from the multivariate Hawkes process theory for MTPP, using learnable per-type neural kernels to modulate query, key and value projections, thereby replacing the corresponding parts in the traditional attention. Benefited from the design, Hawkes Attention unifies event timing and content interaction, learning both the time-relevant behavior and type-specific excitation patterns from the data. The experimental results show that our method achieves better performance compared to the baselines. In addition to the general MTPP, our attention mechanism can also be easily applied to specific temporal structures, such as time series forecasting.

翻译：标记时间点过程（MTPPs）在医疗、社交、商业和金融领域自然涌现。然而，现有的基于Transformer的方法大多仅通过位置编码注入时间信息，依赖共享或参数化的衰减结构，这限制了其捕捉异构和类型特异性时间效应的能力。受此观察启发，我们从多元霍克斯过程理论出发，为MTPP推导出一种称为霍克斯注意力的新型注意力算子，该算子使用可学习的每类型神经核来调制查询、键和值投影，从而替代传统注意力中的相应部分。得益于该设计，霍克斯注意力统一了事件时序与内容交互，从数据中同时学习时间相关行为和类型特异性激励模式。实验结果表明，与基线方法相比，我们的方法取得了更优的性能。除了通用MTPP外，我们的注意力机制也能轻松应用于特定时间结构，例如时间序列预测。