Publikation
StepDP: A Step Towards Expressive and Pervasive Dialogue Platforms
Julian Wolter; Niko Kleer; Michael Feld
In: Elmar Nöth; Alevs Horák; Petr Sojka (Hrsg.). 27th International Conference, TSD 2024, Brno, Czech Republic, September 9–13, 2024. International Conference on Text, Speech and Dialogue (TSD-2024), Brno, Czech Republic, Pages 300-312, ISBN 978-3-031-70566-3, Springer International Publishing, Switzerland, 9/2024.
Zusammenfassung
The advancement of human-machine interactions and the expectation to interact with devices in a natural way necessitates the development of multi-modal dialogue systems that can process and respond to various forms of input modalities. Despite effective recognition methods of the different modalities, their integration into reusable frameworks often falls short, impeding rapid development and the long-term maintenance of systems that incorporate multiple modalities. This paper introduces stepDP, a novel and open-source dialogue platform designed to overcome these challenges by facilitating the quick and efficient implementation of multi-modal dialogue systems following a model-driven design paradigm. Well-researched concepts and algorithms were integrated into a framework and fine-tuned to work together seamlessly. One core concept is that the dialogue logic is abstracted from actual input modalities, allowing for the generalisation of dialogue behavior and seamless integration across domains. Emphasizing modularity and flexibility, stepDP allows for the easy integration of new features, for example, the combination of traditional NLU techniques with innovative LLMs, without requiring extensive system modifications. Our platform not only accelerates the development process but also promotes the exploration of new concepts and techniques in human-machine interaction.
Projekte
- MADMACS - Multiadaptives Dialog-Management für Cyber-physische Umgebungen
- TRACTAT - Transfer of Control (ToC) zwischen Autonomen Systemen und Menschen
- CAMELOT - Kontinuierliches adaptives maschinelles Lernen für Kontrollübergabe-Situationen