Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call. The time span between events can carry important information about the sequence dependence of human behaviors. In this work, we propose a set of methods for using time in sequence prediction. Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization. We also introduce two methods for using next event duration as regularization for training a sequence prediction model. We discuss these methods based on recurrent neural nets. We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks. The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.
The authors present a model base on an RNN to predict marks and duration of events in a temporal point process. The main innovation of the paper is a new representation of a point process with duration (which could also be understood as marks), which allows them to use a "time mask", following the idea of word mask introduced by Choi et al, 2016. In Addition to the mask, the authors also propose a discretization of the duration using one hot encoding and using the event duration as a regularizer. They compare their method to several variations of their own method, two trivial baselines, and one state of the art method (RMTPP) using several real-world datasets and report small gains with respect to that state of the art method.
Overall, the technical contribution of the paper is minor, the gains in performance with respect to a single state of the art are minimal, and the authors oversell their contribution specially in comparison with the related literature. More specifically, my concerns, which prevent me from recommending acceptance, are as follows:
- The authors assume the point process contains duration and intervals, however, point processes generally do not have duration per event but they are discrete events localized in particular time points. Moreover, the duration in their representation (Figure 1) is sometimes an interevent time and sometimes a duration, which makes the whole construction inconsistent. Moreover, what happens then to the representation depicted in Figure 1 when duration is nonexistent or zero?
- The use of "time mask" is not properly justified and the authors are just extending the idea of word mask to their setting -- it is unclear why the duration of an event is going to provide context and in any case this seems like a minor technical contribution. 
- The use of a time mask does not appear "more principled" than previous work (Due et al., Mei & Esiner, Xiao et al.). Previous work use the framework of temporal point processes in a principled way, the current work does not. I would encourage to authors to tone down their language.
- The regularization proposed by the authors uses a Gaussian on the "prediction error" of the duration or just cross entropy on a discretization of the duration. Given the inconsistency in the definition of the duration (sometimes it is duration, sometimes is interevent time), the resulting regularization may lead to unexpected/undesirable results. Moreover, it is unclear why the authors do not model the duration time with an appropriate distribution (e.g., Weibull) and add the log-likelihood of the durations under that distribution as regularization. 
- The difference in performance  with respect to a single nontrivial baseline (the remaining baselines are trivial or versions of their own model) is minimal. Moreover, the authors fail to compare with other methods, e.g., the method by Mei & Eisner, which beats RMTPP. This is specially surprising since the authors mention such work in the related work and there is available source code at https://github.com/HMEIatJHU/neurawkes.