Moritz Völkel


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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.

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GRAIN-S: Manually Annotated Syntax for German Interviews
Agnieszka Falenska | Zoltán Czesznak | Kerstin Jung | Moritz Völkel | Wolfgang Seeker | Jonas Kuhn
Proceedings of the 12th Language Resources and Evaluation Conference

We present GRAIN-S, a set of manually created syntactic annotations for radio interviews in German. The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. The rare combination of gold- and silver-standard annotation layers coming from GRAIN with high-quality syntax trees can serve as a useful resource for speech- and text-based research. Moreover, since interviews can be put between carefully prepared speech and spontaneous conversational speech, they cover phenomena not seen in traditional newspaper-based treebanks. Therefore, GRAIN-S can contribute to research into techniques for model adaptation and for building more corpus-independent tools. GRAIN-S follows TIGER, one of the established syntactic treebanks of German. We describe the annotation process and discuss decisions necessary to adapt the original TIGER guidelines to the interviews domain. Next, we give details on the conversion from TIGER-style trees to dependency trees. We provide data statistics and demonstrate differences between the new dataset and existing out-of-domain test sets annotated with TIGER syntactic structures. Finally, we provide baseline parsing results for further comparison.


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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.