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

Transformer Embeddings of Irregularly Spaced Events and Their Participants