End-to-End Task-Completion Neural Dialogue Systems

An implementation of the
End-to-End Task-Completion Neural Dialogue Systems and A User Simulator for Task-Completion Dialogues.

This document describes how to run the simulation and different dialogue agents (rule-based, command line, reinforcement learning). More instructions to plug in your customized agents or user simulators are in the Recipe section of the paper.

Content

• Data
• Parameter
• Running Dialogue Agents
• Evaluation
• Reference

Data

all the data is under this folder: ./src/deep_dialog/data

• Movie Knowledge Bases
  movie_kb.1k.p --- 94% success rate (for user_goals_first_turn_template_subsets.v1.p)
  movie_kb.v2.p --- 36% success rate (for user_goals_first_turn_template_subsets.v1.p)

• User Goals
  user_goals_first_turn_template.v2.p --- user goals extracted from the first user turn
  user_goals_first_turn_template.part.movie.v1.p --- a subset of user goals [Please use this one, the upper bound success rate on movie_kb.1k.json is 0.9765.]

• NLG Rule Template
  dia_act_nl_pairs.v6.json --- some predefined NLG rule templates for both User simulator and Agent.

• Dialog Act Intent
  dia_acts.txt

• Dialog Act Slot
  slot_set.txt

Parameter

Basic setting

--agt: the agent id
--usr: the user (simulator) id
--max_turn: maximum turns
--episodes: how many dialogues to run
--slot_err_prob: slot level err probability
--slot_err_mode: which kind of slot err mode
--intent_err_prob: intent level err probability

Data setting

--movie_kb_path: the movie kb path for agent side
--goal_file_path: the user goal file path for user simulator side

Model setting

--dqn_hidden_size: hidden size for RL (DQN) agent
--batch_size: batch size for DQN training
--simulation_epoch_size: how many dialogue to be simulated in one epoch
--warm_start: use rule policy to fill the experience replay buffer at the beginning
--warm_start_epochs: how many dialogues to run in the warm start

Display setting

--run_mode: 0 for display mode (NL); 1 for debug mode (Dia_Act); 2 for debug mode (Dia_Act and NL); >3 for no display (i.e. training)
--act_level: 0 for user simulator is Dia_Act level; 1 for user simulator is NL level
--auto_suggest: 0 for no auto_suggest; 1 for auto_suggest
--cmd_input_mode: 0 for NL input; 1 for Dia_Act input. (this parameter is for AgentCmd only)

Others

--write_model_dir: the directory to write the models
--trained_model_path: the path of the trained RL agent model; load the trained model for prediction purpose.

--learning_phase: train/test/all, default is all. You can split the user goal set into train and test set, or do not split (all); We introduce some randomness at the first sampled user action, even for the same user goal, the generated dialogue might be different.

Running Dialogue Agents

Rule Agent

python run.py --agt 5 --usr 1 --max_turn 40
	      --episodes 150
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --intent_err_prob 0.00
	      --slot_err_prob 0.00
	      --episodes 500
	      --act_level 0

Cmd Agent

NL Input

python run.py --agt 0 --usr 1 --max_turn 40
	      --episodes 150
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --intent_err_prob 0.00
	      --slot_err_prob 0.00
	      --episodes 500
	      --act_level 0
	      --run_mode 0
	      --cmd_input_mode 0

Dia_Act Input

python run.py --agt 0 --usr 1 --max_turn 40
	      --episodes 150
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p 
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --intent_err_prob 0.00
	      --slot_err_prob 0.00
	      --episodes 500
	      --act_level 0
	      --run_mode 0
	      --cmd_input_mode 1

End2End RL Agent

Train End2End RL Agent without NLU and NLG (with simulated noise in NLU)

python run.py --agt 9 --usr 1 --max_turn 40
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --dqn_hidden_size 80
	      --experience_replay_pool_size 1000
	      --episodes 500
	      --simulation_epoch_size 100
	      --write_model_dir ./deep_dialog/checkpoints/rl_agent/
	      --run_mode 3
	      --act_level 0
	      --slot_err_prob 0.00
	      --intent_err_prob 0.00
	      --batch_size 16
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --warm_start 1
	      --warm_start_epochs 120

Train End2End RL Agent with NLU and NLG

python run.py --agt 9 --usr 1 --max_turn 40
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --dqn_hidden_size 80
	      --experience_replay_pool_size 1000
	      --episodes 500
	      --simulation_epoch_size 100
	      --write_model_dir ./deep_dialog/checkpoints/rl_agent/
	      --run_mode 3
	      --act_level 1
	      --slot_err_prob 0.00
	      --intent_err_prob 0.00
	      --batch_size 16
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --warm_start 1
	      --warm_start_epochs 120

Test RL Agent with N dialogues:

python run.py --agt 9 --usr 1 --max_turn 40
	      --movie_kb_path ./deep_dialog/data/movie_kb.1k.p
	      --dqn_hidden_size 80
	      --experience_replay_pool_size 1000
	      --episodes 300 
	      --simulation_epoch_size 100
	      --write_model_dir ./deep_dialog/checkpoints/rl_agent/
	      --slot_err_prob 0.00
	      --intent_err_prob 0.00
	      --batch_size 16
	      --goal_file_path ./deep_dialog/data/user_goals_first_turn_template.part.movie.v1.p
	      --trained_model_path ./deep_dialog/checkpoints/rl_agent/noe2e/agt_9_478_500_0.98000.p
	      --run_mode 3

Evaluation

To evaluate the performance of agents, three metrics are available: success rate, average reward, average turns. Here we show the learning curve with success rate.

1. Plotting Learning Curve python draw_learning_curve.py --result_file ./deep_dialog/checkpoints/rl_agent/noe2e/agt_9_performance_records.json
2. Pull out the numbers and draw the curves in Excel

Reference

Main papers to be cited

@inproceedings{li2017end,
  title={End-to-End Task-Completion Neural Dialogue Systems},
  author={Li, Xuijun and Chen, Yun-Nung and Li, Lihong and Gao, Jianfeng and Celikyilmaz, Asli},
  booktitle={Proceedings of The 8th International Joint Conference on Natural Language Processing},
  year={2017}
}

@article{li2016user,
  title={A User Simulator for Task-Completion Dialogues},
  author={Li, Xiujun and Lipton, Zachary C and Dhingra, Bhuwan and Li, Lihong and Gao, Jianfeng and Chen, Yun-Nung},
  journal={arXiv preprint arXiv:1612.05688},
  year={2016}
}