The full codes for this project are not provided due to copyright regulations. Codes not available include the construction of conversation graph in data preprocessing, and a few other modelling methods with copyrights reserved by Huawei. The core codes for this part refer to the following paper (Gritta et al., 2021) https://arxiv.org/abs/2010.15411 .
Hence, the current codes in this repository are not capable to carry out full training with error raised due to the above reason. A Jupyter notebook M2M(Movie)_demo.ipynb is provided alongside to show the results of a simple testrun. Again, this notebook would need to be run with the pieces of codes censored at the current stage.
The complete version will be available and updated when the codes for the above paper become open sourced.
Step 1: Download datasets from https://github.com/ConvLab/ConvLab/tree/master/data/multiwoz
(files: test.json.zip, val.json.zip, train.json.zip)
Step 2: Unzip and add test.json, val.json, train.json to the main folder.
Step 3: Create empty folders in main with names: Data1, Data2, Data3.
Step 4: Set the source folder to the main folder.
Step 5: Run multiwoz/multiwoz_save_data.py to save the subgraph data into Data1, Data2, Data3 for later training. (This takes some time to run for depth_limit > 2)
Step 6: Run GNN_evaluation.py for training. Change model, depth of the subgraph, hyperparameters, etc. in GNN_evaluation.py
The above steps are for the MultiWOZ dataset. The two M2M datasets follow similar procedures with codes in the selfplay folder. Run selfplay/evaluation_selfplay_gnn.py for training.
Libraries needed: pytorch, torch_geometric, networkx (torch_geometric requires to install torch-scatter, torch-sparse and torch-cluster beforehand)