  Temporal Point Processes (TPP) are probabilistic generative frameworks. They
model discrete event sequences localized in continuous time. Generally,
real-life events reveal descriptive information, known as marks. Marked TPPs
model time and marks of the event together for practical relevance. Conditioned
on past events, marked TPPs aim to learn the joint distribution of the time and
the mark of the next event. For simplicity, conditionally independent TPP
models assume time and marks are independent given event history. They
factorize the conditional joint distribution of time and mark into the product
of individual conditional distributions. This structural limitation in the
design of TPP models hurt the predictive performance on entangled time and mark
interactions. In this work, we model the conditional inter-dependence of time
and mark to overcome the limitations of conditionally independent models. We
construct a multivariate TPP conditioning the time distribution on the current
event mark in addition to past events. Besides the conventional intensity-based
models for conditional joint distribution, we also draw on flexible
intensity-free TPP models from the literature. The proposed TPP models
outperform conditionally independent and dependent models in standard
prediction tasks. Our experimentation on various datasets with multiple
evaluation metrics highlights the merit of the proposed approach.
