This paper proposes a framework to improve the typing experience of mobile
users in morphologically rich languages. Smartphone keyboards typically support
features such as input decoding, corrections and predictions that all rely on
language models. For latency reasons, these operations happen on device, so the
models are of limited size and cannot easily cover all the words needed by
users for their daily tasks, especially in morphologically rich languages. In
particular, the compounding nature of Germanic languages makes their vocabulary
virtually infinite. Similarly, heavily inflecting and agglutinative languages
(e.g. Slavic, Turkic or Finno-Ugric languages) tend to have much larger
vocabularies than morphologically simpler languages, such as English or
Mandarin. We propose to model such languages with automatically selected
subword units annotated with what we call binding types, allowing the decoder
to know when to bind subword units into words. We show that this method brings
around 20% word error rate reduction in a variety of compounding languages.
This is more than twice the improvement we previously obtained with a more
basic approach, also described in the paper.

本文提出了一个框架来提高手机用户在形态丰富的语言中的打字体验，该方法使用自动选择的子词单元来对德语等复杂的语言进行建模，并通过绑定类型注释来确定何时将子词单元绑定成单词，此方法在多种复杂语言中表现出约 20% 的错误率降低，是基本方法改进的两倍以上。

移动键盘输入中的复合处理

Handling Compounding in Mobile Keyboard Input

Modern mobile neural networks with a reduced number of weights and parameters
do a good job with image classification tasks, but even they may be too complex
to be implemented in an FPGA for video processing tasks. The article proposes
neural network architecture for the practical task of recognizing images from a
camera, which has several advantages in terms of speed. This is achieved by
reducing the number of weights, moving from a floating-point to a fixed-point
arithmetic, and due to a number of hardware-level optimizations associated with
storing weights in blocks, a shift register, and an adjustable number of
convolutional blocks that work in parallel. The article also proposed methods
for adapting the existing data set for solving a different task. As the
experiments showed, the proposed neural network copes well with real-time video
processing even on the cheap FPGAs.

该论文提出了一种用于实际任务的神经网络结构，用于图像识别，它通过减少参数的数量，使用固定点算术，将权重存储在块、移位寄存器和可调卷积块中，并提供适应现有数据集以解决不同任务的方法，可以在廉价 FPGA 上实现实时视频处理。