Conference paper
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland, 2022 May, pp. 8748--8760
Assistant Professor in Computer Science with focus on “Databases and Data Engineering”
APA
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Wambsganss, T., & Niklaus, C. (2022). Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 8748–8760). Dublin, Ireland: Association for Computational Linguistics.
Chicago/Turabian
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Wambsganss, Thiemo, and Christina Niklaus. “Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8748–8760. Dublin, Ireland: Association for Computational Linguistics, 2022.
MLA
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Wambsganss, Thiemo, and Christina Niklaus. “Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2022, pp. 8748–60.
BibTeX Click to copy
@inproceedings{wambsganss2022a,
title = {Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing},
year = {2022},
month = may,
address = {Dublin, Ireland},
pages = {8748--8760},
publisher = {Association for Computational Linguistics},
author = {Wambsganss, Thiemo and Niklaus, Christina},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month_numeric = {5}
}
We introduce an argumentation annotation approach to model the structure of argumentative discourse in student-written business model pitches. Additionally, the annotation scheme captures a series of persuasiveness scores such as the specificity, strength, evidence, and relevance of the pitch and the individual components. Based on this scheme, we annotated a corpus of 200 business model pitches in German. Moreover, we trained predictive models to detect argumentative discourse structures and embedded them in an adaptive writing support system for students that provides them with individual argumentation feedback independent of an instructor, time, and location. We evaluated our tool in a real-world writing exercise and found promising results for the measured self-efficacy and perceived ease-ofuse. Finally, we present our freely available corpus of persuasive business model pitches with 3,207 annotated sentences in German language and our annotation guidelines.