• Deep Reinforcement Learning for Dialogue Generation

• A Diversity-Promoting Objective Function for Neural Conversation Models

For better reusability and modularization of our code we use ParlAI.

Scripts are available for initalizing the environment on linux and windows in the corresponding directories.

./linux/setup.sh

or

./windows/setup.ps1

To obtain an initial model with supervised learning, run the train.sh or train.ps1 scripts. After obtaining an initial policy for the reinforcement learning based fine-tuning, run reinforce.sh or reinforce.ps1 with the same parameters as the pre-training script.

./linux/train.sh --task dailydialog --model seq2seq

./linux/reinforce.sh --task dailydialog --model seq2seq

reinforce.sh will load the model pre-trained model from either the default checkpoints/<model_name> directory or the one provided in the optional --model_file.