Detecting Attackable Sentences in Arguments

Yohan Jo, Seojin Bang, Emaad Manzoor, Eduard Hovy, Chris Reed


Abstract
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in online arguments. We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences. We demonstrate that a sentence’s attackability is associated with many of these characteristics regarding the sentence’s content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability. Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople.
Anthology ID:
2020.emnlp-main.1
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–23
Language:
URL:
https://aclanthology.org/2020.emnlp-main.1
DOI:
10.18653/v1/2020.emnlp-main.1
Bibkey:
Cite (ACL):
Yohan Jo, Seojin Bang, Emaad Manzoor, Eduard Hovy, and Chris Reed. 2020. Detecting Attackable Sentences in Arguments. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1–23, Online. Association for Computational Linguistics.
Cite (Informal):
Detecting Attackable Sentences in Arguments (Jo et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.1.pdf
Video:
 https://slideslive.com/38938707
Code
 yohanjo/emnlp20_arg_attack