Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers) |
| Erscheinungsort | Melbourne |
| Herausgeber (Verlag) | Association for Computational Linguistics (ACL) |
| Seiten | 2545-2555 |
| Seitenumfang | 11 |
| ISBN (Print) | 9781948087322 |
| Publikationsstatus | Veröffentlicht - Juli 2018 |
| Extern publiziert | Ja |
| Veranstaltung | 56th Annual Meeting of the Association for Computational Linguistics - Melbourne, Australien Dauer: 15 Juli 2018 → 20 Juli 2018 |
Abstract
This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Theoretische Informatik und Mathematik
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Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers). Melbourne: Association for Computational Linguistics (ACL), 2018. S. 2545-2555.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Modeling Deliberative Argumentation Strategies on Wikipedia
AU - Al-Khatib, Khalid
AU - Wachsmuth, Henning
AU - Lang, Kevin
AU - Herpel, Jakob
AU - Hagen, Matthias
AU - Stein, Benno
PY - 2018/7
Y1 - 2018/7
N2 - This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.
AB - This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.
UR - http://www.scopus.com/inward/record.url?scp=85063085562&partnerID=8YFLogxK
U2 - 10.18653/v1/p18-1237
DO - 10.18653/v1/p18-1237
M3 - Conference contribution
AN - SCOPUS:85063085562
SN - 9781948087322
SP - 2545
EP - 2555
BT - Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers)
PB - Association for Computational Linguistics (ACL)
CY - Melbourne
T2 - 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Y2 - 15 July 2018 through 20 July 2018
ER -