本文比较研究了逻辑回归、卷积神经网络(CNN)、长短时记忆网络(LSTM)和准循环神经网络(QRNN)等问题分类的方法。所有的模型都使用了预先训练过的GLoVe模型（一种无监督学习算法）嵌入，并对人工标记的数据进行了训练。使用CNN模型，5个卷积层和不同大小的内核并行堆叠，然后是一个完全连接的层，可以获得最佳的精度。该模型在TREC 10测试集上的准确率达到90.7%。本文所有的模型架构都是在PyTorch上从零开始开发的，少数情况下基于可靠的开源实现。
原文标题：Artificial Intelligence: Question Type Classification Methods Comparison
The paper presents a comparative study of state-of-the-art approaches for question classification task: Logistic Regression, Convolutional Neural Networks (CNN), Long Short-Term Memory Network (LSTM) and Quasi-Recurrent Neural Networks (QRNN). All models use pre-trained GLoVe word embeddings and trained on human-labeled data. The best accuracy is achieved using CNN model with five convolutional layers and various kernel sizes stacked in parallel, followed by one fully connected layer. The model reached 90.7% accuracy on TREC 10 test set. All the model architectures in this paper were developed from scratch on PyTorch, in few cases based on reliable open-source implementation.
原文作者：Tamirlan Seidakhmetov
原文链接： https://arxiv.org/abs/2001.00571
问题类型分类的方法比较（AI).pdf