Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume |
| Pages | 224-233 |
| Number of pages | 10 |
| Publication status | Published - Apr 2021 |
| Externally published | Yes |
| Event | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online Duration: 19 Apr 2021 → 23 Apr 2021 |
Abstract
When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances. Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computational Theory and Mathematics
- Social Sciences(all)
- Linguistics and Language
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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021. p. 224-233.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Belief-based Generation of Argumentative Claims
AU - Alshomary, Milad
AU - Chen, Wei Fan
AU - Gurcke, Timon
AU - Wachsmuth, Henning
N1 - Funding Information: We thank the anonymous reviewers for their helpful feedback. This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center “On-The-Fly Computing” (SFB 901/3) under the project number 160364472.
PY - 2021/4
Y1 - 2021/4
N2 - When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances. Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.
AB - When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances. Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.
UR - http://www.scopus.com/inward/record.url?scp=85107287949&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2101.09765
DO - 10.48550/arXiv.2101.09765
M3 - Conference contribution
AN - SCOPUS:85107287949
SN - 9781954085022
SP - 224
EP - 233
BT - Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
T2 - 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
Y2 - 19 April 2021 through 23 April 2021
ER -