论文标题
问题类型分类方法比较
Question Type Classification Methods Comparison
论文作者
论文摘要
本文介绍了问题分类任务的最先进方法的比较研究：逻辑回归，卷积神经网络（CNN），长期短期记忆网络（LSTM）和准循环神经网络（QRNN）。所有模型都使用预训练的手套词嵌入并接受了人体标记的数据训练。使用CNN模型具有五个卷积层和并联堆叠的各种内核尺寸，然后是一个完全连接的层，可以实现最佳精度。该模型在TREC 10测试集上达到90.7％的精度。本文中的所有模型体系结构都是在Pytorch上从头开始开发的，在少数情况下，基于可靠的开源实现。
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.
