Question Type Classification Methods Comparison. (arXiv:2001.00571v1 [cs.CL])

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.

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