Established recurrent neural networks are well-suited to solve a wide variety
of prediction tasks involving discrete sequences. However, they do not perform
as well in the task of dynamical system identification, when dealing with
observations from continuous variables that are unevenly sampled in time, for
example due to missing observations. We show how such neural sequence models
can be adapted to deal with variable step sizes in a natural way. In
particular, we introduce a time-aware and stationary extension of existing
models (including the Gated Recurrent Unit) that allows them to deal with
unevenly sampled system observations by adapting to the observation times,
while facilitating higher-order temporal behavior. We discuss the properties
and demonstrate the validity of the proposed approach, based on samples from
two industrial input/output processes.

本文介绍了一种基于时间的递归神经网络扩展模型，使其适用于不均匀采样的连续变量观测数据，并展示了该模型在两种工业输入 / 输出过程的样本中的有效性。

利用时序感知神经序列模型进行系统辨识

System Identification with Time-Aware Neural Sequence Models

To capture salient contextual information for spoken language understanding
(SLU) of a dialogue, we propose time-aware models that automatically learn the
latent time-decay function of the history without a manual time-decay function.
We also propose a method to identify and label the current speaker to improve
the SLU accuracy. In experiments on the benchmark dataset used in Dialog State
Tracking Challenge 4, the proposed models achieved significantly higher F1
scores than the state-of-the-art contextual models. Finally, we analyze the
effectiveness of the introduced models in detail. The analysis demonstrates
that the proposed methods were effective to improve SLU accuracy individually.

提出了一种自动学习历史潜在时间衰减函数的时间感知模型，用于捕捉对话中的显著上下文信息，并提出了一种识别和标记当前发言者的方法来提高 SLU 准确性。在 Dialog State Tracking Challenge 4 的基准数据集上进行实验证明，所提出的模型的 F1 得分显著高于最先进的上下文模型。最后，对引入模型的有效性进行了详细分析，结果表明它们在改善 SLU 准确性方面取得了良好效果。