Data-driven models have demonstrated state-of-the-art performance in
inferring the temporal ordering of events in text. However, these models often
overlook explicit temporal signals, such as dates and time windows. Rule-based
methods can be used to identify the temporal links between these time
expressions (timexes), but they fail to capture timexes' interactions with
events and are hard to integrate with the distributed representations of neural
net models. In this paper, we introduce a framework to infuse temporal
awareness into such models by learning a pre-trained model to embed timexes. We
generate synthetic data consisting of pairs of timexes, then train a character
LSTM to learn embeddings and classify the timexes' temporal relation. We
evaluate the utility of these embeddings in the context of a strong neural
model for event temporal ordering, and show a small increase in performance on
the MATRES dataset and more substantial gains on an automatically collected
dataset with more frequent event-timex interactions.

本文介绍一种框架来将时间感知融入模型，方法是通过学习预先训练的模型嵌入时间表达式，生成由时间表达式和相关事件组成的合成数据，使用字符 LSTM 学习嵌入并分类时间表达式的时间关系，在强神经模型的上下文中评估嵌入的效用，并在 MATRES 数据集上表现出微小的提高，对于具有更频繁的事件时间交互的自动收集数据集则提供了更实质性的收益。

为深度时序排序模型嵌入时间表达式

Embedding time expressions for deep temporal ordering models

Deep Learning is arguably the most rapidly evolving research area in recent
years. As a result it is not surprising that the design of state-of-the-art
deep neural net models proceeds without much consideration of the latest
hardware targets, and the design of neural net accelerators proceeds without
much consideration of the characteristics of the latest deep neural net models.
Nevertheless, in this paper we show that there are significant improvements
available if deep neural net models and neural net accelerators are
co-designed.

本文研究如何进行神经网络模型和加速器的协同设计，展示了这样的设计可以大幅提升深度学习的性能。