Nav-NNDial is the extended version of NN-Dial (https://github.com/shawnwun/NNDIAL), the original open source toolkit for building end-to-end trainable task-oriented dialogue models. It was modified by to be able to consider the context of multi-tasking.
In order to run the program, here are a list of packages with the suggested versions,
- Theano 0.8.2
- Numpy 1.12.0
- Scipy 0.16.1
- NLTK 3.0.0
- OpenBLAS
- NLTK stopwords corpus
- NLTK wordnet
-
Camrest676: Collected in Wen et al, 2017a. The dataset is publicly available at: https://www.repository.cam.ac.uk/handle/1810/260970
-
KvretNav: The navigation domain subset of the KVRET dataset in Eric & Manning, 2017. The full dataset is publicly available at: https://nlp.stanford.edu/blog/a-new-multi-turn-multi-domain-task-oriented-dialogue-dataset/
-
NavDial: The multi-task navigation domain dataset. It is publicy available at: https://github.com/garygsw/NavDial-dataset
The model can be roughtly divided into encoder and decoder modules.
* The encoder modules contain:
- LSTM encoder : an LSTM network that encodes the user utterance.
- RNN+CNN tracker : a set of slot trackers that keep track of each slot/value pair across turns.
- DB operator : a discrete database accessing component.
* The decoder modules contain:
- Policy network : a decision-making module that produces the conditional vector for decoding.
- LSTM decoder : an LSTM network that generates the system response.
This software encloses the work from three publications, Wen et al, 2016, 2017a, 2017b. The models/methods supported in this software are listed below,
- The NDM model with a deterministic policy network (Wen et al 2017a).
- The Attention-based NDM model (Wen et al 2017a).
- Various decoder implementations and snapshot learning (Wen et al 2016).
- The LIDM model with a latent policy network and Reinforcement Learning (Wen et al 2017b).
Below are configuration parameters explained by sections:
* [learn] // hyperparamters for model learning
- lr : initial learning rate of Adam.
- lr_decay : learning rate decay.
- stop_count : the maximum of early stopping steps.
the maximum number of times when validation gets worse.
- cur_stop_count: current early stopping step.
- l2 : l2 regularisation weight.
- random_seed : random seed.
- min_impr : the relative minimal improvement allowed.
- debug : debug flag. // not properly implemented
- llogp : log prob in the last epoch.
- grad_clip : gradient clipping parameter.
* [file] // file paths
- db : database file.
- ontology : ontology, which defines the value scope of each slot.
if no values are specified, the values will be automatically loaded from DB.
- corpus : the corpus file for training the model.
- semi : semantic dictionary for delexicalisation.
- model : the path of the produced model file.
* [data] // data manipulation
- split : the proportion of data in training, validation, and testing sets.
- percent : the percentage of train/valid used.
- shuffle : shuffle mode, either static or dynamic
static: typical shuffling, do not reassign training and validation sets.
dynamic: when shuffling, shuffle the entire training+validation set and re-divide the two sets.
this is the trick to get better tracking performance on a small dataset.
- lengthen : either 0 or 1
0 : do not lengthen the dialogue. Default option.
1 : lengthen the dialogue by appending one randomly selected dialogue from the training set.
this is the trick to get better tracking performance on a small dataset.
* [mode] // training mode: trk|encdec|all
- learn_mode : the mode of training.
trk : pre-train the trackers
encdec: train the model except the trackers.
all : train the entire model jointly, not properly tested/implemented.
* [n2n] // components of network
- encoder : the encoder type.
- tracker : the tracker type.
- decoder : the decoder type.
* [enc] // structure of encoder
- ihidden : the size of the encoder hidden layer.
* [trk] // structure of tracker
- informable : whether to use informable trackers. Default yes.
- requestable : whether to use requestable trackers. Default yes.
- belief : the belief state type used for decoding. Best choice: summary.
- trkenc : tracker encoder type. Default cnn.
- wvec : pre-trained word vectors. Default none.
* [dec] // structure of decoder
- ohidden : the size of the decoder hidden layer
- struct : the decoder structures. Types are [lstm_lm|lstm_cond|lstm_mix].
Please check Wen et al, 2016 for more detail.
- snapshot : whether to use snapshot learning.
- wvec : pre-trained word vectors.
* [ply] // structure of policy network
- policy : the policy network type.
normal : a simple MLP.
attention : an MLP w/ attention mechansim on tracker outputs.
latent : the latent policy, used in LIDM.
- latent : the latent action space, used in LIDM only.
* [gen] // generation, repeat penalty: inf|none
- alpha : the weight for additional reward during decoding.
- verbose : verbose level. // not properly implemented
- topk : decode up to "topk" responses.
- beamwidth : the beamwidth during decoding.
- repeat_penalty: the additional penality when encoutering repeating slot tokens.
- token_reward : a heuristic reward used in Wen et al, 2017a.
The training of the model is done in two steps. Firstly, train the belief tracker using a tracker config file,
Note:
- use
nndial.pyto use the Camrest676 dataset - use
kvret_nndial.pyto use the KvretNav dataset - use
nav_nndial.pyto use the NavDial dataset - there are also special config files created for each dataset
// Run the tracker training first
python nndial.py -config config/tracker.cfg -mode train
Now train an NDM based on the pre-trained tracker,
// Copy the pre-trained tracker model, and continue to train the other parts
cp model/CamRest.tracker-example.model model/CamRest.NDM.model
python nndial.py -config config/NDM.cfg -mode adjust
Once you have the model trained, you can validate or test its performance,
// Run the evaluation on the validation set for model selection
python nndial.py -config config/NDM.cfg -mode valid
// Run the evaluation on the test set to access the model performance
python nndial.py -config config/NDM.cfg -mode test
Or interact with it directly to see how it does,
python nndial.py -config config/NDM.cfg -mode interact
If you want an attention-based NDM model, just modified the config file,
cp model/CamRest.tracker-example.model model/CamRest.Att-NDM.model
python nndial.py -config config/Att-NDM.cfg -mode adjust
Or you can train an LIDM using semi-supervised variational inference,
cp model/CamRest.tracker-example.model model/CamRest.LIDM.model
python nndial.py -config config/LIDM.cfg -mode adjust
You can also choose to refine the LIDM policy network by corpus-based RL,
cp model/CamRest.LIDM.model model/CamRest.LIDM-RL.model
python nndial.py -config config/LIDM-RL.cfg -mode rl
The commands listed here are just examples. Please refer to scp/example_run.sh for more detail. Note, each new config file could change the intended model architecture, therefore, prompt the model to re-initiate the model parameters. For example, when training trackers it doesn't matter the structure of decoder and encoder because we can change it in the next config file.
[Wen et al, 2017a]
@InProceedings{wenN2N17,
author = {Wen, Tsung-Hsien and Vandyke, David and Mrk\v{s}i\'{c}, Nikola and
Gasic, Milica and Rojas Barahona, Lina M. and Su, Pei-Hao and
Ultes, Stefan and Young, Steve},
title = {A Network-based End-to-End Trainable Task-oriented Dialogue System},
booktitle = {EACL},
month = {April},
year = {2017},
address = {Valencia, Spain},
publisher = {Association for Computational Linguistics},
pages = {438--449},
url = {http://www.aclweb.org/anthology/E17-1042}
}
[Wen et al, 2017b]
@inproceedings{wenLIDM17,
title = {Latent Intention Dialogue Models},
Author = {Wen, Tsung-Hsien and Miao, Yishu and Blunsom, Phil and Young, Steve},
booktitle = {ICML},
series = {ICML'17},
year = {2017},
location = {Sydney, Australia},
numpages = {10},
publisher = {JMLR.org},
}
[Wen et al, 2016]
@InProceedings{wenEMNLP2016,
author = {Wen, Tsung-Hsien and Gasic, Milica and Mrk\v{s}i\'{c}, Nikola and
Rojas Barahona, Lina M. and Su, Pei-Hao and Ultes, Stefan and
Vandyke, David and Young, Steve},
title = {Conditional Generation and Snapshot Learning in Neural Dialogue Systems},
booktitle = {EMNLP},
month = {November},
year = {2016},
address = {Austin, Texas},
publisher = {ACL},
pages = {2153--2162},
url = {https://aclweb.org/anthology/D16-1233}
}
[Eric & Manning, 2017]
@InProceedings{Eric2017,
arxivId = {1705.05414},
author = {Eric, Mihail and Manning, Christopher D.},
booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
eprint = {1705.05414},
pages = {37--49},
publisher = {Association for Computational Linguistics},
title = {{Key-Value Retrieval Networks for Task-Oriented Dialogue}},
url = {http://arxiv.org/abs/1705.05414},
year = {2017}
}