• Corpus ID: 11512678

A Convolutional Architecture for Word Sequence Prediction

@inproceedings{Wang2015ACA,
  title={A Convolutional Architecture for Word Sequence Prediction},
  author={Mingxuan Wang and Zhengdong Lu and Hang Li and Wenbin Jiang and Qun Liu},
  year={2015}
}

• Mingxuan Wang, Zhengdong Lu, +2 authors Qun Liu
• Published 2015
• Computer Science

We propose a convolutional neural network, named genCNN, for word sequence prediction. Different from previous work on neural networkbased language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. Instead, we use a convolutional neural network to predict the next word with the history of words of variable length. Also different from the existing feedforward networks for language modeling, our model can effectively fuse the local correlation and global correlation in the word sequence, with a convolution-gating strategy specifically designed for the task. We argue that our model can give adequate representation of the history, and therefore can naturally exploit both the short and long range dependencies. Our model is fast, easy to train, and readily parallelized. Our extensive experiments on text generation and n-best re-ranking in machine translation show that genCNN outperforms the state-ofthe-arts with big margins.