Papers by Maximilian Schmidt
ADVISER: A Dialog System Framework for Education & Research (P19-3)
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Daniel Ortega, Dirk Väth, Gianna Weber, Lindsey Vanderlyn, Maximilian Schmidt, Moritz Völkel, Zorica Karacevic, Ngoc Thang Vu
| Challenge: | In this paper, we focus on task-oriented dialog systems, although our framework allows easy integration of non-task dialog systems and their combination. |
| Approach: | They propose an open source dialog system framework for education and research that supports multi-domain task-oriented conversations in two languages. |
| Outcome: | The proposed framework supports multi-domain task-oriented conversations in two languages and is open source for education and research. |
Prompting-based Synthetic Data Generation for Few-Shot Question Answering (2024.lrec-main)
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| Challenge: | Language models have boosted the performance of Question Answering, but data annotation is costly. |
| Approach: | They propose to use large language models to improve Question Answering performance . they argue that domain-agnostic knowledge from LMs is sufficient to create a well-curated dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on few-shot Question Answering. |
ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents (2020.acl-demos)
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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
| Challenge: | Existing toolkits for developing dialog systems are limited to core components and do not support multi-modal processing and social signals. |
| Approach: | They propose to use ADVISER to develop multi-modal dialog agents using multi-text and social signals. |
| Outcome: | The proposed toolkit is flexible, easy to use, and easy to extend for linguists and cognitive scientists, thereby providing a flexible platform for collaborative research. |