Maximilian Schmidt


2020

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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.

2019

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ADVISER: A Dialog System Framework for Education & Research
Daniel Ortega | Dirk Väth | Gianna Weber | Lindsey Vanderlyn | Maximilian Schmidt | Moritz Völkel | Zorica Karacevic | Ngoc Thang Vu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

In this paper, we present ADVISER - an open source dialog system framework for education and research purposes. This system supports multi-domain task-oriented conversations in two languages. It additionally provides a flexible architecture in which modules can be arbitrarily combined or exchanged - allowing for easy switching between rules-based and neural network based implementations. Furthermore, ADVISER offers a transparent, user-friendly framework designed for interdisciplinary collaboration: from a flexible back end, allowing easy integration of new features, to an intuitive graphical user interface supporting nontechnical users.