Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics |
| Editors | Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault |
| Pages | 4334-4345 |
| Number of pages | 12 |
| ISBN (electronic) | 9781952148255 |
| Publication status | Published - Jul 2020 |
| Externally published | Yes |
| Event | The 58th Annual Meeting of the Association for Computational Linguistics - Online, Unknown Duration: 5 Jul 2020 → 10 Jul 2020 |
Publication series
| Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
|---|---|
| ISSN (Print) | 0736-587X |
Abstract
In argumentation, people state premises to reason towards a conclusion. The conclusion conveys a stance towards some target, such as a concept or statement. Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons. However, the conclusion is key to understanding an argument, and hence, to any application that processes argumentation. We thus study the question to what extent an argument's conclusion can be reconstructed from its premises. In particular, we argue here that a decisive step is to infer a conclusion's target, and we hypothesize that this target is related to the premises' targets. We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network. Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines. According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Computer Science Applications
- Social Sciences(all)
- Linguistics and Language
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Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ed. / Dan Jurafsky; Joyce Chai; Natalie Schluter; Joel Tetreault. 2020. p. 4334-4345 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Target Inference in Argument Conclusion Generation
AU - Alshomary, Milad
AU - Syed, Shahbaz
AU - Potthast, Martin
AU - Wachsmuth, Henning
N1 - Publisher Copyright: © 2020 Association for Computational Linguistics
PY - 2020/7
Y1 - 2020/7
N2 - In argumentation, people state premises to reason towards a conclusion. The conclusion conveys a stance towards some target, such as a concept or statement. Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons. However, the conclusion is key to understanding an argument, and hence, to any application that processes argumentation. We thus study the question to what extent an argument's conclusion can be reconstructed from its premises. In particular, we argue here that a decisive step is to infer a conclusion's target, and we hypothesize that this target is related to the premises' targets. We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network. Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines. According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.
AB - In argumentation, people state premises to reason towards a conclusion. The conclusion conveys a stance towards some target, such as a concept or statement. Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons. However, the conclusion is key to understanding an argument, and hence, to any application that processes argumentation. We thus study the question to what extent an argument's conclusion can be reconstructed from its premises. In particular, we argue here that a decisive step is to infer a conclusion's target, and we hypothesize that this target is related to the premises' targets. We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network. Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines. According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.
UR - http://www.scopus.com/inward/record.url?scp=85101468404&partnerID=8YFLogxK
U2 - 10.18653/v1/2020.acl-main.399
DO - 10.18653/v1/2020.acl-main.399
M3 - Conference contribution
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 4334
EP - 4345
BT - Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
A2 - Jurafsky, Dan
A2 - Chai, Joyce
A2 - Schluter, Natalie
A2 - Tetreault, Joel
T2 - The 58th Annual Meeting of the Association for Computational Linguistics
Y2 - 5 July 2020 through 10 July 2020
ER -