This repo contains a PyTorch implementation of the End-to-End Memory Network as described in the paper Learning end-to-end goal-oriented dialog. Also there is a code for replicating the results on T1-T5 bAbI tasks and a jupyter notebook file for visualizing memory attentions of a learned model.
- python 3.6
- pytorch 0.3.0
First you need to download bAbI dialog dataset.
To run the training, use the following pattern:
python train.py /path/to/dataset/train_set_file.txt /path/to/dataset/dev_set_file.txt /path/to/dataset/candidates_file.txt
There are different command line arguments for adjusting model and training parameters. For complete list, run
python train.py -h
For evaluation, use:
python eval.py /path/to/saved/model/dir /path/to/dataset/test_set_file.txt