Need help with Hierarchical-Neural-Autoencoder?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

213 Stars 65 Forks 21 Commits 4 Opened issues

Services available


Need anything else?

Contributors list

No Data

A Hierarchical Neural Autoencoder for Paragraphs and Documents

Implementations of the three models presented in the paper "A Hierarchical Neural Autoencoder for Paragraphs and Documents" by Jiwei Li, Minh-Thang Luong and Dan Jurafsky, ACL 2015



matlab >= 2014b

memory >= 4GB


Standard_LSTM: Standard LSTM Autoencoder

hier_LSTM: Hierarchical LSTM Autoencoder

hierLSTMAttention: Hierarchical LSTM Autoencoder with Attention

DownLoad Data

  • dictionary
    : vocabulary
  • train_permute.txt
    : training data for standard Model. Each line corresponds to one document/paragraph
  • train_source_permute_segment.txt
    : source training data for hierarchical Models. Each line corresponds to one sentence. An empty line starts a new document/sentence. Documents are reversed.
  • test_source_permute_segment.txt
    : target training data for hierarchical Model.

Training roughly takes 2-3 weeks for standard models and 4-6 weeks for hierarchical models on a K40 GPU machine.

For any question or bug with the code, feel free to contact [email protected]

    title={A Hierarchical Neural Autoencoder for Paragraphs and Documents},
    author={Li, Jiwei and Luong, Minh-Thang and Jurafsky, Dan},
    journal={arXiv preprint arXiv:1506.01057},

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.