  This paper presents an extensive comparative study of four neural network
models, including feed-forward networks, convolutional networks, recurrent
networks and long short-term memory networks, on two sentence classification
datasets of English and Vietnamese text. We show that on the English dataset,
the convolutional network models without any feature engineering outperform
some competitive sentence classifiers with rich hand-crafted linguistic
features. We demonstrate that the GloVe word embeddings are consistently better
than both Skip-gram word embeddings and word count vectors. We also show the
superiority of convolutional neural network models on a Vietnamese newspaper
sentence dataset over strong baseline models. Our experimental results suggest
some good practices for applying neural network models in sentence
classification.
