Claim Optimization in Computational Argumentation

Gabriella Skitalinskaya, Maximilian Spliethöver, Henning Wachsmuth

Abstract
An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given debate. Prior work has studied the automatic assessment of argument quality extensively. Yet, no approach actually improves the quality so far. To fill this gap, this paper proposes the task of claim optimization: to rewrite argumentative claims in order to optimize their delivery. As multiple types of optimization are possible, we approach this task by first generating a diverse set of candidate claims using a large language model, such as BART, taking into account contextual information. Then, the best candidate is selected using various quality metrics. In automatic and human evaluation on an English-language corpus, our quality-based candidate selection outperforms several baselines, improving 60% of all claims (worsening 16% only). Follow-up analyses reveal that, beyond copy editing, our approach often specifies claims with details, whereas it adds less evidence than humans do. Moreover, its capabilities generalize well to other domains, such as instructional texts.
Anthology ID:
2023.inlg-main.10
Volume:
Proceedings of the 16th International Natural Language Generation Conference
Month:
September
Year:
2023
Address:
Prague, Czechia
Editors:
C. Maria Keet, Hung-Yi Lee, Sina Zarrieß
Venues:
INLG | SIGDIAL
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–152
Language:
URL:
https://aclanthology.org/2023.inlg-main.10
DOI:
10.18653/v1/2023.inlg-main.10
Bibkey:
Cite (ACL):
Gabriella Skitalinskaya, Maximilian Spliethöver, and Henning Wachsmuth. 2023. Claim Optimization in Computational Argumentation. In Proceedings of the 16th International Natural Language Generation Conference, pages 134–152, Prague, Czechia. Association for Computational Linguistics.
Cite (Informal):
Claim Optimization in Computational Argumentation (Skitalinskaya et al., INLG-SIGDIAL 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2023.inlg-main.10.pdf
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