Question Paraphrase Identification (QPI) is a critical task for large-scale
Question-Answering forums. The purpose of QPI is to determine whether a given
pair of questions are semantically identical or not. Previous approaches for
this task have yielded promising results, but have often relied on complex
recurrence mechanisms that are expensive and time-consuming in nature. In this
paper, we propose a novel architecture combining a Bidirectional Transformer
Encoder with Convolutional Neural Networks for the QPI task. We produce the
predictions from the proposed architecture using two different inference
setups: Siamese and Matched Aggregation. Experimental results demonstrate that
our model achieves state-of-the-art performance on the Quora Question Pairs
dataset. We empirically prove that the addition of convolution layers to the
model architecture improves the results in both inference setups. We also
investigate the impact of partial and complete fine-tuning and analyze the
trade-off between computational power and accuracy in the process. Based on the
obtained results, we conclude that the Matched-Aggregation setup consistently
outperforms the Siamese setup. Our work provides insights into what
architecture combinations and setups are likely to produce better results for
the QPI task.

本文提出了一种结合双向转换编码器和卷积神经网络的新架构来处理 QPI 任务，其中使用了 Siamese 和 Matched Aggregation 两种推断设置，经实验证明，在这两种推断设置下，该模型在 Quora Question Pairs 数据集上实现了最新的最佳性能，并分析了部分和完全微调的影响以及计算能力与准确性之间的权衡。

基于上下文嵌入的卷积神经网络用于重复问题识别

Contextualized Embeddings based Convolutional Neural Networks for  Duplicate Question Identification

We develop a novel optical neural network (ONN) framework which introduces a
degree of scalar invariance to image classification estima- tion. Taking a hint
from the human eye, which has higher resolution near the center of the retina,
images are broken out into multiple levels of varying zoom based on a focal
point. Each level is passed through an identical convolutional neural network
(CNN) in a Siamese fashion, and the results are recombined to produce a high
accuracy estimate of the object class. ONNs act as a wrapper around existing
CNNs, and can thus be applied to many existing algorithms to produce notable
accuracy improvements without having to change the underlying architecture.

本文提出一种新的光学神经网络（ONN）框架，模拟人眼视网膜中心附近的高分辨率，实现了图像分类估计的标量不变性，并且通过同维孪生（Siamese）结构将不同缩放级别的图像输入通过相同的卷积神经网络（CNN）进行处理，最终的结果可用于提高现有算法中的准确性。

光学神经网络

Optical Neural Networks

Various hand-crafted features and metric learning methods prevail in the
field of person re-identification. Compared to these methods, this paper
proposes a more general way that can learn a similarity metric from image
pixels directly. By using a "siamese" deep neural network, the proposed method
can jointly learn the color feature, texture feature and metric in a unified
framework. The network has a symmetry structure with two sub-networks which are
connected by Cosine function. To deal with the big variations of person images,
binomial deviance is used to evaluate the cost between similarities and labels,
which is proved to be robust to outliers.
Compared to existing researches, a more practical setting is studied in the
experiments that is training and test on different datasets (cross dataset
person re-identification). Both in "intra dataset" and "cross dataset"
settings, the superiorities of the proposed method are illustrated on VIPeR and
PRID.

本研究介绍了一种使用深度神经网络学习图像相似度度量的方法，该方法采用 “连体” 结构体系，可以统一学习颜色特征、纹理特征和度量。论文还比较了不同数据集上训练和测试结果，结果表明该方法在人员再识别方面性能显著优于现有方法。