Patrick L. Lange

Also published as: Patrick Lange


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Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women
Kai-Hui Liang | Patrick Lange | Yoo Jung Oh | Jingwen Zhang | Yoshimi Fukuoka | Zhou Yu
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.


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My Turn To Read: An Interleaved E-book Reading Tool for Developing and Struggling Readers
Nitin Madnani | Beata Beigman Klebanov | Anastassia Loukina | Binod Gyawali | Patrick Lange | John Sabatini | Michael Flor
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Literacy is crucial for functioning in modern society. It underpins everything from educational attainment and employment opportunities to health outcomes. We describe My Turn To Read, an app that uses interleaved reading to help developing and struggling readers improve reading skills while reading for meaning and pleasure. We hypothesize that the longer-term impact of the app will be to help users become better, more confident readers with an increased stamina for extended reading. We describe the technology and present preliminary evidence in support of this hypothesis.


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Leveraging Multimodal Dialog Technology for the Design of Automated and Interactive Student Agents for Teacher Training
David Pautler | Vikram Ramanarayanan | Kirby Cofino | Patrick Lange | David Suendermann-Oeft
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

We present a paradigm for interactive teacher training that leverages multimodal dialog technology to puppeteer custom-designed embodied conversational agents (ECAs) in student roles. We used the open-source multimodal dialog system HALEF to implement a small-group classroom math discussion involving Venn diagrams where a human teacher candidate has to interact with two student ECAs whose actions are controlled by the dialog system. Such an automated paradigm has the potential to be extended and scaled to a wide range of interactive simulation scenarios in education, medicine, and business where group interaction training is essential.


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LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications
Alexei V. Ivanov | Patrick L. Lange | David Suendermann-Oeft
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue