We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g. emotion recognition, engagement level prediction and backchanneling) conversational agents. The final Python-based implementation of our toolkit is flexible, easy to use, and easy to extend not only for technically experienced users, such as machine learning researchers, but also for less technically experienced users, such as linguists or cognitive scientists, thereby providing a flexible platform for collaborative research. Link to open-source code: https://github.com/DigitalPhonetics/adviser
@article{arxiv.2005.01777,
title = {ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents},
author = {Chia-Yu Li and Daniel Ortega and Dirk Väth and Florian Lux and Lindsey Vanderlyn and Maximilian Schmidt and Michael Neumann and Moritz Völkel and Pavel Denisov and Sabrina Jenne and Zorica Kacarevic and Ngoc Thang Vu},
journal= {arXiv preprint arXiv:2005.01777},
year = {2020}
}
Comments
All authors contributed equally. Accepted to be presented at ACL - System demonstrations - 2020