We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification. MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.

本文提出了一种新颖的卷积神经网络架构-多组范数约束卷积神经网络（MGNC-CNN），它利用多组单词嵌入进行句子分类，并采用了一个组规范策略，该策略对与各自单词嵌入集生成的子组件相关的权重进行不同的惩罚。结果表明，MGNC-CNN在分类任务上优于基线模型，且比其他相似的网络结构更简单且训练时间更少。

MGNC-CNN: 利用多个词向量的简单方法进行句子分类