KB-InfoBot

This repository contains all the code and data accompanying the paper Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access.

Prerequisites

See requirements.txt for required packacges. Also download nltk data:

python -m nltk.downloader all

IMPORTANT: Download the data and pretrained models from here, unpack the tar and place it at the root of the repository.

Code Organization

All agents are in deep_dialog/agents/ directory
The user-simulator along with a template based and seq2seq NLG is in deep_dialog/usersims/ directory
deep_dialog/dialog_system/ contains classes for dialog manager and the database

Interact with the pre-trained InfoBot!

$ python interact.py

This will launch the command line tool running the RL-SoftKB infobot trained on the "Medium-KB" split. Instructions on how to interact the system are displayed within the tool itself. You can also specify other agents to test:

$ python interact.py --help
usage: interact.py [-h] [--agent AGENT]

optional arguments:
  -h, --help     show this help message and exit
  --agent AGENT  Agent to run -- (rule-no / rl-no / rule-hard / rl-hard /
                 rule-soft / rl-soft / e2e-soft

Training

To train the RL agents, call train.py with the following options:

$ python train.py --help
usage: train.py [-h] [--agent AGENT_TYPE] [--db DB] [--model_name MODEL_NAME]
                [--N N] [--max_turn MAX_TURN] [--nlg_temp NLG_TEMP]
                [--max_first_turn MAX_FIRST_TURN] [--err_prob ERR_PROB]
                [--dontknow_prob DONTKNOW_PROB] [--sub_prob SUB_PROB]
                [--reload RELOAD]

optional arguments:
  -h, --help            show this help message and exit
  --agent AGENT_TYPE    agent to use (rl-no / rl-hard / rl-soft / e2e-soft)
  --db DB               imdb-(S/M/L/XL) -- This is the KB split to use, e.g.
                        imdb-M
  --model_name MODEL_NAME
                        model name to save
  --N N                 Number of simulations
  --max_turn MAX_TURN   maximum length of each dialog (default=20, 0=no
                        maximum length)
  --nlg_temp NLG_TEMP   Natural Language Generator softmax temperature (to
                        control noise)
  --max_first_turn MAX_FIRST_TURN
                        Maximum number of slots informed by user in first turn
  --err_prob ERR_PROB   the probability of the user simulator corrupting a
                        slot value
  --dontknow_prob DONTKNOW_PROB
                        the probability that user simulator does not know a
                        slot value
  --sub_prob SUB_PROB   the probability that user simulator substitutes a slot
                        value
  --reload RELOAD       Reload previously saved model (0-no, 1-yes)

Example:

python train.py --agent e2e-soft --db imdb-M --model_name e2e_soft_example.m

Testing

To evaluate both RL and Rule agents, call sim.py with the following options:

$ python sim.py --help
usage: sim.py [-h] [--agent AGENT_TYPE] [--N N] [--db DB]
              [--max_turn MAX_TURN] [--err_prob ERR_PROB]
              [--dontknow_prob DONTKNOW_PROB] [--sub_prob SUB_PROB]
              [--nlg_temp NLG_TEMP] [--max_first_turn MAX_FIRST_TURN]
              [--model_name MODEL_NAME]

optional arguments:
  -h, --help            show this help message and exit
  --agent AGENT_TYPE    agent to use (rule-no / rl-no / rule-hard / rl-hard /
                        rule-soft / rl-soft / e2e-soft)
  --N N                 Number of simulations
  --db DB               imdb-(S/M/L/XL) -- This is the KB split to use, e.g.
                        imdb-M
  --max_turn MAX_TURN   maximum length of each dialog (default=20, 0=no
                        maximum length)
  --err_prob ERR_PROB   the probability of the user simulator corrupting a
                        slot value
  --dontknow_prob DONTKNOW_PROB
                        the probability that user simulator does not know a
                        slot value
  --sub_prob SUB_PROB   the probability that user simulator substitutes a slot
                        value
  --nlg_temp NLG_TEMP   Natural Language Generator softmax temperature (to
                        control noise)
  --max_first_turn MAX_FIRST_TURN
                        Maximum number of slots informed by user in first turn
  --model_name MODEL_NAME
                        model name to evaluate (This should be the same as
                        what you gave for training). Pass "pretrained" to use
                        pretrained models.

Run without the --model_name argument to test on pre-trained models. Example:

python sim.py --agent rl-soft --db imdb-M

Hyperparameters

The default hyperparameters for each KB split are in settings/config_<db_name>.py. These include:

RL agent options-

nhid: Number of hidden units
batch: Batch size
ment: Entropy regularization parameter
lr: Learning rate for initial supervised learning of policy. RL learning rate is fixed to 0.005.
featN: Only for end-to-end RL agent, n for n-gram feature extraction
pol_start: Number of supervised learning updates before switching to RL
input: Input type to the policy network - full/entropy
sl: Only for end-to-end RL agent, Type of supervised learning (bel-only belief tracker, pol-only policy, e2e (default)-both)
rl: Only for end-to-end RL agent, Type of reinforcement learning (bel-only belief tracker, pol-only policy, e2e (default)-both)

Rule agent options-

tr: Threshold for databse entropy to inform
ts: Threshold for slot entropy to request
max_req: Maximum requests allowed per slot
frac: Ratio to initial slot entropy, below which if the slot entropy falls it is not requested anymore
upd: Update count for bayesian belief tracking

Note

Make sure to add THEANO_FLAGS=device=cpu,floatX=float32 before any command if you are running on a CPU.

Contributors

If you use this code please cite the following:

Dhingra, B., Li, L., Li, X., Gao, J., Chen, Y. N., Ahmed, F., & Deng, L. (2017). Towards End-to-end reinforcement learning of dialogue agents for information access. ACL.

@inproceedings{dhingra2017towards,
  title={Towards End-to-end reinforcement learning of dialogue agents for information access},
  author={Dhingra, Bhuwan and Li, Lihong and Li, Xiujun and Gao, Jianfeng and Chen, Yun-Nung and Ahmed, Faisal and Deng, Li},
  booktitle={Proceddings of ACL},
  year={2017}
}

Report bugs and missing info to bdhingraATandrewDOTcmuDOTedu (replace AT, DOT appropriately).

MiuLab / KB-InfoBot