Corpus ID: 2723946

A Convolutional Attention Network for Extreme Summarization of Source Code

@article{Allamanis2016ACA,
  title={A Convolutional Attention Network for Extreme Summarization of Source Code},
  author={Miltiadis Allamanis and Hao Peng and Charles Sutton},
  journal={ArXiv},
  year={2016},
  volume={abs/1602.03001}
}

• Miltiadis Allamanis, Hao Peng, Charles Sutton
• Published 2016
• Computer Science, Mathematics
• ArXiv

Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical… Expand

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