The ability to accurately represent sentences is central to language understanding. We
describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network …

This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative
sentence modeling. Our models leverage either constituency trees or dependency trees of …

The goal of sentence and document modeling is to accurately represent the meaning of
sentences and documents for various Natural Language Processing tasks. In this work, we …

Neural network models have been demonstrated to be capable of achieving remarkable
performance in sentence and document modeling. Convolutional neural network (CNN) and …

Abstract Convolutional Neural Networks (CNNs) have shown to yield very strong results in
several Computer Vision tasks. Their application to language has received much less …

In sentence modeling and classification, convolutional neural network approaches have
recently achieved state-of-the-art results, but all such efforts process word vectors …

We propose MVCNN, a convolution neural network (CNN) architecture for sentence
classification. It (i) combines diverse versions of pretrained word embeddings and (ii) …

In this paper, we introduce a generic inference hybrid framework for Conv olutional R
ecurrent N eural N etwork (conv-RNN) of semantic modeling of text, seamless integrating the …

In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural
networks (RNNs) from attention mechanisms. We hypothesize that this is because the …

Convolutional neural network has been proven to be a powerful semantic composition
model for modelling sentences. A standard convolutional neural network usually consists of …