This fork is based on the work Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog by Qin et al. 2020. Please refer to the original repository for information on that paper. I used this codebase to carry out further experiments for our paper:
@article{WahdeVirgolin2022DAISY,
author = {Mattias Wahde and Marco Virgolin},
title = {DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI},
journal = {International Journal of Human–Computer Interaction},
volume = {0},
number = {0},
pages = {1-18},
year = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/10447318.2022.2081762},
URL = {https://doi.org/10.1080/10447318.2022.2081762},
eprint = {https://doi.org/10.1080/10447318.2022.2081762}
}
Changes include:
- Modifications to generate and use a smaller version of the data sets considered in the paper (SMD aka KVR, and MultiWOZ 2.1), see
generate_our_test.py
and files referenced there. - Minor changes to the original code base to test DF-Net on such data sets.
- Code to query GPT-3 on those data sets, using OpenAI's API.