NegotiationToM

Improving Dialog Systems for Negotiation with Personality Modeling. Implements our ToM algorithms.

Our code for internal using is available in another repo.

A more clear code with proper instruction will be available soon here.

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Installation

Dependencies

0. Clone code from github:

git clone git@github.com:princeton-nlp/NegotiationToM.git
cd NegotiationToM 

1. Create a conda environment:

conda create --name <env> --file tom_conda10.txt

2. install pytorch

# For Linux
# CUDA 9.0
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch

# CUDA 10.0
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

# CPU Only
conda install pytorch-cpu==1.1.0 torchvision-cpu==0.3.0 cpuonly -c pytorch

• Check official website for more installation instructions .

3. pip install additional dependencies

# install other dependecies
pip install -r requirements_cuda10.txt
# install our code
pip install -e .

Dependence Reference

• Python 3.5, pytorch=1.1.0

  • For cuda9: pytorch=1.1.0=py3.5_cuda9.0.176_cudnn7.5.1_0
  • For cuda10: pytorch=1.1.0=py3.5_cuda10.0.130_cudnn7.5.1_0

• Additional dependencies:

  • tom_new.txt, requirements.txt
  • For cuda10: tom_cuda10.txt, requirements_cuda10.txt

Traning instruction

Preprocess

PYTHONPATH=. python core/price_tracker.py --train-examples-path data/train-luis-clean2.json --output data/price_tracker.pkl

Train a Supervised Learning Agent

bash craigslistbargain/exp_scripts/identifier/old/train_sl.sh

Train a Reinforcement Learning Agent

• Generate scenarios

PYTHONPATH=. python ../scripts/chat_to_scenarios.py --chats data/train-luis-post.json --scenarios data/train-scenarios.json
PYTHONPATH=. python ../scripts/chat_to_scenarios.py --chats data/dev-luis-post.json --scenarios data/dev-scenarios.json

• Train the RL model

  parameter <use_gpu>: "--gpuid 0" for gpu, "" for cpu.

bash exp_scripts/rl/train_a2c.sh <exp_name> <use_gpu> <seed> <learning_rate> 

Train a ToM model

• Sample data

bash exp_scripts/identifier/sample_data.sh <use_gpu> <seed> 

• Implicit Model

bash exp_scripts/identifier/train_uttr_history_tom.sh <exp_name> <use_gpu>

• Explicit Model

bash exp_scripts/identifier/train_uttr_id_history_tom.sh <exp_name> <use_gpu>

Evaluate result of different model.

For evaluation process, gpu acceleration is unnecessary.

Parameter <1/beta> equal to $\frac{1}{beta}$ of tom inference process in our paper.

• Rule Model

bash exp_scripts/rl/eval_rule.sh <exp_name> "" <seed> <1/beta> "${CHECK_POINT}"

• SL Model

bash exp_scripts/rl/eval_sl.sh <exp_name> "" <seed> <1/beta> "${CHECK_POINT}" 

• RL Model

bash exp_scripts/rl/eval_rl.sh <exp_name> "" <seed> <1/beta> "${CHECK_POINT}" 

• Implicit ToM Model

bash exp_scripts/rl/eval_tom_noid.sh <exp_name> "" <seed> <1/beta>" "${CHECK_POINT}" "${TOM_CHECK_POINT}"

• Explicit ToM Model

bash exp_scripts/rl/eval_tom.sh <exp_name> "" <seed> <1/beta> "${CHECK_POINT}" "${TOM_CHECK_POINT}"