  The neural Hawkes process (Mei & Eisner, 2017) is a generative model of
irregularly spaced sequences of discrete events. To handle complex domains with
many event types, Mei et al. (2020a) further consider a setting in which each
event in the sequence updates a deductive database of facts (via
domain-specific pattern-matching rules); future events are then conditioned on
the database contents. They show how to convert such a symbolic system into a
neuro-symbolic continuous-time generative model, in which each database fact
and the possible event has a time-varying embedding that is derived from its
symbolic provenance.
  In this paper, we modify both models, replacing their recurrent LSTM-based
architectures with flatter attention-based architectures (Vaswani et al.,
2017), which are simpler and more parallelizable. This does not appear to hurt
our accuracy, which is comparable to or better than that of the original models
as well as (where applicable) previous attention-based methods (Zuo et al.,
2020; Zhang et al., 2020a).
