multiwoz is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Paweł Budzianowski from Cambridge Dialogue Systems Group under Apache License 2.0.
Python 2 with pip
In repo directory:
To download and pre-process the data run:
python create_delex_data.py
To train the model run:
python train.py [--args=value]
Some of these args include:
// hyperparamters for model learning --max_epochs : numbers of epochs --batch_size : numbers of turns per batch --lr_rate : initial learning rate --clip : size of clipping --l2_norm : l2-regularization weight --dropout : dropout rate --optim : optimization method // network structure --emb_size : word vectors emedding size --use_attn : whether to use attention --hid_size_enc : size of RNN hidden cell --hid_size_pol : size of policy hidden output --hid_size_dec : size of RNN hidden cell --cell_type : specify RNN type To evaluate the run:
python test.py [--args=value]
The following benchmark results were produced by this software. We ran a small grid search over various hyperparameter settings and reported the performance of the best model on the test set. The selection criterion was 0.5match + 0.5success+100*BLEU on the validation set. The final parameters were:
// hyperparamters for model learning --max_epochs : 20 --batch_size : 64 --lr_rate : 0.005 --clip : 5.0 --l2_norm : 0.00001 --dropout : 0.0 --optim : Adam // network structure --emb_size : 50 --use_attn : True --hid_size_enc : 150 --hid_size_pol : 150 --hid_size_dec : 150 --cell_type : lstm If you use any source codes or datasets included in this toolkit in your work, please cite the corresponding papers. The bibtex are listed below:
[Budzianowski et al. 2018] @inproceedings{budzianowski2018large, Author = {Budzianowski, Pawe{\l} and Wen, Tsung-Hsien and Tseng, Bo-Hsiang and Casanueva, I{\~n}igo and Ultes Stefan and Ramadan Osman and Ga{\v{s}}i\'c, Milica}, title={MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2018} } [Ramadan et al. 2018] @inproceedings{ramadan2018large, title={Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing}, author={Ramadan, Osman and Budzianowski, Pawe{\l} and Gasic, Milica}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics}, volume={2}, pages={432--437}, year={2018} } If you have found any bugs in the code, please contact: pfb30 at cam dot ac dot uk