Papers by Lindsey Vanderlyn
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. |
Toward Implicit Reference in Dialog: A Survey of Methods and Data (2022.aacl-main)
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| Challenge: | In natural language, speakers often leave out information that is understood by the other party through the shared context. |
| Approach: | They propose to use omitted entities as implicit references in dialogs to improve language processing. |
| Outcome: | The proposed method is based on a set of experiments which show that the proposed method has a high level of accuracy and is a success. |
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. |
Conversational Tree Search: A New Hybrid Dialog Task (2023.eacl-main)
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| Challenge: | Existing conversational interfaces are limited to FAQs and dialogs, allowing users to search for specific questions. |
| Approach: | They propose a task that bridges the gap between FAQ-style information retrieval and task-oriented dialog. |
| Outcome: | The proposed task bridges the gap between FAQ-style information retrieval and task-oriented dialog. |
It’s What You Say and How You Say It: Investigating the Effect of Linguistic vs. Behavioral Adaptation in Task-Oriented Chatbots (2025.coling-main)
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| Challenge: | linguistic adaptation is not known to have a positive impact on dialog success and user perception. |
| Approach: | They evaluate subjective and objective aspects of dialog success and user perceptions through a user study . they also examine linguistic adaptations of dialog agents to determine which aspects influence user perception . |
| Outcome: | The proposed agents can differ in their level of formality and their linguistic style. |
DIAGRAPH: An Open-Source Graphic Interface for Dialog Flow Design (2023.acl-demo)
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| Challenge: | Dialog systems have gained attention as a convenient way for users to access information in a more personalized manner. |
| Approach: | They present a graphical dialog flow editor built on ADVISER toolkit . it provides a clean and intuitive graphical interface for creating dialog systems . |
| Outcome: | The tool is based on the ADVISER toolkit and is evaluated with subject-experts . it is able to quickly prototype dialog systems and provide a test bed for students learning about dialog systems. |
Towards a Zero-Data, Controllable, Adaptive Dialog System (2024.lrec-main)
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| Challenge: | Recent approaches to controllable dialog systems require additional training data to be deployed in new domains. |
| Approach: | They propose to generate dialog tree data directly from dialog trees by using a commercial Large Language Model or a single GPU. |
| Outcome: | The proposed approach can achieve comparable dialog success to models trained on human data. |