Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) |
| Editors | Marilyn Walker, Heng Ji, Amanda Stent |
| Place of Publication | New Orleans |
| Pages | 1930-1940 |
| Number of pages | 11 |
| Publication status | Published - 2018 |
| Externally published | Yes |
| Event | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 |
Abstract
Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. 1 On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.
ASJC Scopus subject areas
- Social Sciences(all)
- Linguistics and Language
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Computer Science Applications
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Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). ed. / Marilyn Walker; Heng Ji; Amanda Stent. New Orleans, 2018. p. 1930-1940.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - The Argument Reasoning Comprehension Task
T2 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
AU - Habernal, Ivan
AU - Wachsmuth, Henning
AU - Gurevych, Iryna
AU - Stein, Benno
N1 - Funding Information: This work has been supported by the ArguAna Project GU 798/20-1 (DFG), and by the DFG-funded research training group “Adaptive Preparation of Information form Heterogeneous Sources” (AIPHES, GRK 1994/1).
PY - 2018
Y1 - 2018
N2 - Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. 1 On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.
AB - Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. 1 On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.
UR - http://www.scopus.com/inward/record.url?scp=85064116274&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1708.01425
DO - 10.48550/arXiv.1708.01425
M3 - Conference contribution
AN - SCOPUS:85064116274
SN - 9781948087278
SP - 1930
EP - 1940
BT - Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
A2 - Walker, Marilyn
A2 - Ji, Heng
A2 - Stent, Amanda
CY - New Orleans
Y2 - 1 June 2018 through 6 June 2018
ER -