@inproceedings{li-etal-2020-adviser,
title = "{ADVISER}: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents",
author = {Li, Chia-Yu and
Ortega, Daniel and
V{\"a}th, Dirk and
Lux, Florian and
Vanderlyn, Lindsey and
Schmidt, Maximilian and
Neumann, Michael and
V{\"o}lkel, Moritz and
Denisov, Pavel and
Jenne, Sabrina and
Kacarevic, Zorica and
Vu, Ngoc Thang},
editor = "Celikyilmaz, Asli and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.31",
doi = "10.18653/v1/2020.acl-demos.31",
pages = "279--286",
abstract = "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.",
}
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%0 Conference Proceedings
%T ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
%A Li, Chia-Yu
%A Ortega, Daniel
%A Väth, Dirk
%A Lux, Florian
%A Vanderlyn, Lindsey
%A Schmidt, Maximilian
%A Neumann, Michael
%A Völkel, Moritz
%A Denisov, Pavel
%A Jenne, Sabrina
%A Kacarevic, Zorica
%A Vu, Ngoc Thang
%Y Celikyilmaz, Asli
%Y Wen, Tsung-Hsien
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-adviser
%X 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.
%R 10.18653/v1/2020.acl-demos.31
%U https://aclanthology.org/2020.acl-demos.31
%U https://doi.org/10.18653/v1/2020.acl-demos.31
%P 279-286
Markdown (Informal)
[ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents](https://aclanthology.org/2020.acl-demos.31) (Li et al., ACL 2020)
ACL
- Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, and Ngoc Thang Vu. 2020. ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 279–286, Online. Association for Computational Linguistics.