Details
| Original language | English |
|---|---|
| Title of host publication | Same Side Stance Classification Shared Task 2019 |
| Subtitle of host publication | Proceedings of the Same Side Stance Classification Shared Task organized as a part of the 6th Workshop on Argument Mining (ArgMining 2019) and co-located with the the 57th Annual Meeting of the Association for Computational Linguistics (ACL19) |
| Pages | 1-7 |
| Number of pages | 7 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2019 Same Side Stance Classification Shared Task, Same Side Shared Task 2019 - Florence, Italy Duration: 1 Aug 2019 → 1 Aug 2019 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR Workshop Proceedings |
| Volume | 2921 |
| ISSN (Print) | 1613-0073 |
Abstract
This paper introduces the Same Side Stance Classification problem and reports on the outcome of a related shared task, which has been collocated with the Sixth Workshop on Argument Mining at the ACL 2019 in Florence.1 We have proposed this task as a variant of the well-known stance classification task: Instead of predicting for a single argument whether it has a positive or negative stance towards a given topic, same side classification ‘merely’ involves the prediction of whether two given arguments share the same stance. The paper in hand provides the rationale for proposing this task, overviews important related work, describes the developed datasets, and reports on the results along with the main methods of the nine submitted systems. We draw conclusions from these results with respect to the suitability of the task as a proxy for measuring progress in the field of argument mining.
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Same Side Stance Classification Shared Task 2019: Proceedings of the Same Side Stance Classification Shared Task organized as a part of the 6th Workshop on Argument Mining (ArgMining 2019) and co-located with the the 57th Annual Meeting of the Association for Computational Linguistics (ACL19) . 2021. p. 1-7 (CEUR Workshop Proceedings; Vol. 2921).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Same side stance classification
AU - Stein, Benno
AU - Ajjour, Yamen
AU - El Baff, Roxanne
AU - Al-Khatib, Khalid
AU - Cimiano, Philipp
AU - Wachsmuth, Henning
PY - 2021
Y1 - 2021
N2 - This paper introduces the Same Side Stance Classification problem and reports on the outcome of a related shared task, which has been collocated with the Sixth Workshop on Argument Mining at the ACL 2019 in Florence.1 We have proposed this task as a variant of the well-known stance classification task: Instead of predicting for a single argument whether it has a positive or negative stance towards a given topic, same side classification ‘merely’ involves the prediction of whether two given arguments share the same stance. The paper in hand provides the rationale for proposing this task, overviews important related work, describes the developed datasets, and reports on the results along with the main methods of the nine submitted systems. We draw conclusions from these results with respect to the suitability of the task as a proxy for measuring progress in the field of argument mining.
AB - This paper introduces the Same Side Stance Classification problem and reports on the outcome of a related shared task, which has been collocated with the Sixth Workshop on Argument Mining at the ACL 2019 in Florence.1 We have proposed this task as a variant of the well-known stance classification task: Instead of predicting for a single argument whether it has a positive or negative stance towards a given topic, same side classification ‘merely’ involves the prediction of whether two given arguments share the same stance. The paper in hand provides the rationale for proposing this task, overviews important related work, describes the developed datasets, and reports on the results along with the main methods of the nine submitted systems. We draw conclusions from these results with respect to the suitability of the task as a proxy for measuring progress in the field of argument mining.
UR - http://www.scopus.com/inward/record.url?scp=85112205858&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85112205858
T3 - CEUR Workshop Proceedings
SP - 1
EP - 7
BT - Same Side Stance Classification Shared Task 2019
T2 - 2019 Same Side Stance Classification Shared Task, Same Side Shared Task 2019
Y2 - 1 August 2019 through 1 August 2019
ER -