@inproceedings{wambsganss-niklaus-2022-modeling,
title = "Modeling Persuasive Discourse to Adaptively Support Students{'} Argumentative Writing",
author = "Wambsganss, Thiemo and
Niklaus, Christina",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.599",
doi = "10.18653/v1/2022.acl-long.599",
pages = "8748--8760",
abstract = "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-of-use. Finally, we present our freely available corpus of persuasive business model pitches with 3,207 annotated sentences in German language and our annotation guidelines.",
}
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%0 Conference Proceedings
%T Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing
%A Wambsganss, Thiemo
%A Niklaus, Christina
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F wambsganss-niklaus-2022-modeling
%X 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-of-use. Finally, we present our freely available corpus of persuasive business model pitches with 3,207 annotated sentences in German language and our annotation guidelines.
%R 10.18653/v1/2022.acl-long.599
%U https://aclanthology.org/2022.acl-long.599
%U https://doi.org/10.18653/v1/2022.acl-long.599
%P 8748-8760
Markdown (Informal)
[Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing](https://aclanthology.org/2022.acl-long.599) (Wambsganss & Niklaus, ACL 2022)
ACL