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
GPU
matlab >= 2014b
memory >= 4GB
Standard_LSTM: Standard LSTM Autoencoder
hier_LSTM: Hierarchical LSTM Autoencoder
hier_LSTM_Attention: Hierarchical LSTM Autoencoder with Attention
DownLoad Data
dictionary
: vocabularytrain_permute.txt
: training data for standard Model. Each line corresponds to one document/paragraphtrain_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 jiweil@stanford.edu
@article{li2015hierarchical,
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},
year={2015}
}