Transferable Dialogue Systems and User Simulators

Paper | Full Training | Transfer Learning | Citation | Contact

This repository contains the source code for the paper Transferable Dialogue Systems and User Simulators, which is accepted by ACL-IJCNLP 2021. In this work, we propose a novel joint optimisation framework of both dialogue system and user simulator for multi-domain task-oriented dialogues (MultiWOZ). We further demonstrate the efficacy of our model in two transfer learning setups. Please refer to the paper for more details.

NB: The codebase is not completely clean and still in the process of refactoring.

Requirements

python3.6 and the packages in requirements.txt. Install them via virtual environment:

>>> python -m venv env
>>> source env/bin/activate
>>> pip install -r requirements.txt

Full Training

Pre-process the raw MultiWOZ data

>>> bash src/prepare_data.sh

Training from scratch using Supervised Learning

Run the following command to train both ent-to-end agents.

>>> bash src/train-SL.sh [your_sl_model_name]

your_sl_model_name can be a random string, and the resulting model will be stored at checkpoint/your_sl_model_name/.

Continued training using Reinforcement Learning

Run the following command to fine-tune the model using RL through agents' interaction.

>>> bash src/train-RL.sh [your_sl_model_name] [your_rl_model_name]

your_rl_model_name can be a random string, and the resulting model will be stored at checkpoint/your_rl_model_name/.

Testing the model

Through the following command, the trained dialogue agent will be evaluated through interacting with the test corpus and the user simulator.

>>> bash src/test.sh [your_model_name]

Three output files will be produced for each type of interaction:

DST: predictions of belief state
Policy: predictions of dialogue act
NLG: predictions of natural language response

Results of interacting against test corpus will be saved in corpus_interact_output/.

Results of interacting between two agents will be saved in user_interact_output/.

Test the provided model

We provide a trained model to reproduce the benchmark result reported in the paper (Table2).

First, unzip the model file by

>>> cd checkpoint/
>>> tar zxvf provided_joust.tar.gz
>> cd ..

Then run the testing script with the model name provided_joust

>>> bash src/test.sh provided_joust

Again, the results produced by interacting against the test set and the user simulator will be stored in the folders corpus_interact_output/ and user_interact_output/ respectively.

Note that the produced results by this model is slightly different to the reported numbers (e.g., 95.6 vs 96.0) as they are averaged by three runs.

Transfer Learning

Create transfer learning data

>>> bash src/prepare_data_transfer_learning.sh

Data for single-to-multi domain setup will be created in data/single_to_multi/. Data for domain adaptation setup will be created in data/domain_transfer/.

Once the data is generated, one can run the model training in the following procedure:

train from scratch, with the running mode pretrain.
fine-tune the model using the adaption data, with the running mode finetune.
fine-turn the model using reinforcement learning, with the running mdoe rl.

(Bash scripts of running transfer learning will be added later)

Citation

@inproceedings{tseng-etal-2021-transferable,
    title = "Transferable Dialogue Systems and User Simulators",
    author = "Tseng, Bo-Hsiang  and
      Dai, Yinpei  and
      Kreyssig, Florian  and
      Byrne, Bill",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.13",
    pages = "152--166",
}

Contact Us

Please contact bht26@cam.ac.uk or raise an issue in this repository.

xiami2019 / joust