Computational Argumentation Synthesis as a Language Modeling Task

Roxanne El Baff, Henning Wachsmuth, Khalid Al Khatib, Manfred Stede, Benno Stein


Abstract
Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates. Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations. In this paper, we approach such an argumentation synthesis as a language modeling task. In our language model, argumentative discourse units are the “words”, and arguments represent the “sentences”. Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units’ argumentative roles, and finally “phrases” an argument by instantiating the structure with semantically coherent units from the pool. Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.
Anthology ID:
W19-8607
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–64
Language:
URL:
https://aclanthology.org/W19-8607
DOI:
10.18653/v1/W19-8607
Bibkey:
Cite (ACL):
Roxanne El Baff, Henning Wachsmuth, Khalid Al Khatib, Manfred Stede, and Benno Stein. 2019. Computational Argumentation Synthesis as a Language Modeling Task. In Proceedings of the 12th International Conference on Natural Language Generation, pages 54–64, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Computational Argumentation Synthesis as a Language Modeling Task (El Baff et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8607.pdf