ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
Chia-Yu Li | Daniel Ortega | Dirk Väth | Florian Lux | Lindsey Vanderlyn | Maximilian Schmidt | Michael Neumann | Moritz Völkel | Pavel Denisov | Sabrina Jenne | Zorica Kacarevic | Ngoc Thang Vu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
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.
- Daniel Ortega 1
- Dirk Väth 1
- Florian Lux 1
- Lindsey Vanderlyn 1
- Maximilian Schmidt 1
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