genCNN: A Convolutional Architecture for Word Sequence Prediction
Abstrak
We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based 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 $gen$CNN outperforms the state-of-the-arts with big margins.
Topik & Kata Kunci
Penulis (5)
Mingxuan Wang
Zhengdong Lu
Hang Li
Wenbin Jiang
Qun Liu
Akses Cepat
- Tahun Terbit
- 2015
- Bahasa
- en
- Total Sitasi
- 29×
- Sumber Database
- Semantic Scholar
- DOI
- 10.3115/v1/P15-1151
- Akses
- Open Access ✓