Within the context of event modeling and understanding, we propose a new
method for neural sequence modeling that takes partially-observed sequences of
discrete, external knowledge into account. We construct a sequential neural
variational autoencoder, which uses Gumbel-Softmax reparametrization within a
carefully defined encoder, to allow for successful backpropagation during
training. The core idea is to allow semi-supervised external discrete knowledge
to guide, but not restrict, the variational latent parameters during training.
Our experiments indicate that our approach not only outperforms multiple
baselines and the state-of-the-art in narrative script induction, but also
converges more quickly.

该论文在事件建模和理解的背景下，提出了一种新的神经序列建模方法，该方法考虑了离散的、部分被观察到的外部知识序列。通过构建一个顺序神经变分自编码器，在精心定义的编码器中使用 Gumbel-Softmax 重参数化技术，允许半监督的外部离散知识在训练期间指导但不限制变分潜在参数。实验表明，该方法不仅优于多个基线和叙事脚本归纳的最新技术，而且收敛速度更快。

具有序列、半监督离散变量的事件表示

Event Representation with Sequential, Semi-Supervised Discrete Variables

Temporal point processes are the dominant paradigm for modeling sequences of
events happening at irregular intervals. The standard way of learning in such
models is by estimating the conditional intensity function. However,
parameterizing the intensity function usually incurs several trade-offs. We
show how to overcome the limitations of intensity-based approaches by directly
modeling the conditional distribution of inter-event times. We draw on the
literature on normalizing flows to design models that are flexible and
efficient. We additionally propose a simple mixture model that matches the
flexibility of flow-based models, but also permits sampling and computing
moments in closed form. The proposed models achieve state-of-the-art
performance in standard prediction tasks and are suitable for novel
applications, such as learning sequence embeddings and imputing missing data.

研究如何通过直接建模条件插值时间的条件分布来克服强制使参数化强度函数的局限性，并提出了一个简单的混合模型，其匹配了基于流的模型的灵活性，但也允许闭式采样和计算矩，该模型在标准预测任务中表现出最先进的性能，适用于学习序列嵌入和填补缺失数据等新领域。