  Temporal point process (TPP) is commonly used to model the asynchronous event
sequence featuring occurrence timestamps and revealed by probabilistic models
conditioned on historical impacts.
  While lots of previous works have focused on `goodness-of-fit' of TPP models
by maximizing the likelihood, their predictive performance is unsatisfactory,
which means the timestamps generated by models are far apart from true
observations.
  Recently, deep generative models such as denoising diffusion and score
matching models have achieved great progress in image generating tasks by
demonstrating their capability of generating samples of high quality.
  However, there are no complete and unified works exploring and studying the
potential of generative models in the context of event occurence modeling for
TPP.
  In this work, we try to fill the gap by designing a unified
\textbf{g}enerative framework for \textbf{n}eural \textbf{t}emporal
\textbf{p}oint \textbf{p}rocess (\textsc{GNTPP}) model to explore their
feasibility and effectiveness, and further improve models' predictive
performance.
  Besides, in terms of measuring the historical impacts, we revise the
attentive models which summarize influence from historical events with an
adaptive reweighting term considering events' type relation and time intervals.
  Extensive experiments have been conducted to illustrate the improved
predictive capability of \textsc{GNTPP} with a line of generative probabilistic
decoders, and performance gain from the revised attention.
  To the best of our knowledge, this is the first work that adapts generative
models in a complete unified framework and studies their effectiveness in the
context of TPP.
  Our codebase including all the methods given in Section.5.1.1 is open in
\url{https://github.com/BIRD-TAO/GNTPP}. We hope the code framework can
facilitate future research in Neural TPPs.
