Multi-criteria Chinese word segmentation is a promising but challenging task,
which exploits several different segmentation criteria and mines their common
underlying knowledge. In this paper, we propose a flexible multi-criteria
learning for Chinese word segmentation. Usually, a segmentation criterion could
be decomposed into multiple sub-criteria, which are shareable with other
segmentation criteria. The process of word segmentation is a routing among
these sub-criteria. From this perspective, we present Switch-LSTMs to segment
words, which consist of several long short-term memory neural networks (LSTM),
and a switcher to automatically switch the routing among these LSTMs. With
these auto-switched LSTMs, our model provides a more flexible solution for
multi-criteria CWS, which is also easy to transfer the learned knowledge to new
criteria. Experiments show that our model obtains significant improvements on
eight corpora with heterogeneous segmentation criteria, compared to the
previous method and single-criterion learning.

本文提出了一种灵活的多标准中文分词学习方法：Switch-LSTMs，它由几个长短时记忆神经网络 (LSTM) 和一个自动选择器组成，可以在多个子标准之间灵活切换，相比之前的方法和单一标准学习，该模型在八个具有不同标准的语料库上取得了显著的改进。

用于多标准中文分词的 Switch-LSTMs

Switch-LSTMs for Multi-Criteria Chinese Word Segmentation

Predicting large-scale transportation network traffic has become an important
and challenging topic in recent decades. Inspired by the domain knowledge of
motion prediction, in which the future motion of an object can be predicted
based on previous scenes, we propose a network grid representation method that
can retain the fine-scale structure of a transportation network. Network-wide
traffic speeds are converted into a series of static images and input into a
novel deep architecture, namely, spatiotemporal recurrent convolutional
networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the
advantages of deep convolutional neural networks (DCNNs) and long short-term
memory (LSTM) neural networks. The spatial dependencies of network-wide traffic
can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An
experiment on a Beijing transportation network with 278 links demonstrates that
SRCNs outperform other deep learning-based algorithms in both short-term and
long-term traffic prediction.

提出了一种基于运动预测领域知识的网络网格表示方法，将网络整体交通速度转换为静态图像，并输入一种新的深度体系结构 —— 时空循环卷积网络 (SRCNs)，可以用于交通预测，实验证明其在短期和长期交通预测方面优于其他基于深度学习的算法。

交通网络中时空循环卷积网络用于交通预测

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction  in Transportation Networks

In this short note, we present an extension of long short-term memory (LSTM)
neural networks to using a depth gate to connect memory cells of adjacent
layers. Doing so introduces a linear dependence between lower and upper layer
recurrent units. Importantly, the linear dependence is gated through a gating
function, which we call depth gate. This gate is a function of the lower layer
memory cell, the input to and the past memory cell of this layer. We conducted
experiments and verified that this new architecture of LSTMs was able to
improve machine translation and language modeling performances.

本文提出了使用深度门扩展长短期记忆神经网络模型的方法，借助门控函数来控制较高和较低层次中的循环单元线性相关性，实现了机器翻译和语言建模性能的提高。