Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th Workshop on Argument Mining |
| Pages | 111-114 |
| Publication status | Published - 2022 |
| Event | 9th Workshop on Argument Mining - Gyeongju, Korea, Republic of Duration: 17 Oct 2022 → 17 Oct 2022 |
Abstract
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the 9th Workshop on Argument Mining. 2022. p. 111-114.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Argument Novelty and Validity Assessment via Multitask and Transfer Learning
AU - Alshomary, Milad
AU - Stahl, Maja
PY - 2022
Y1 - 2022
N2 - An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new task to assess the novelty and validity of a conclusion given a set of premises. In this paper, we present a multitask learning approach that transfers the knowledge learned from the natural language inference task to the tasks at hand. Evaluation results indicate the importance of both knowledge transfer and joint learning, placing our approach in the fifth place with strong results compared to baselines.
AB - An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new task to assess the novelty and validity of a conclusion given a set of premises. In this paper, we present a multitask learning approach that transfers the knowledge learned from the natural language inference task to the tasks at hand. Evaluation results indicate the importance of both knowledge transfer and joint learning, placing our approach in the fifth place with strong results compared to baselines.
M3 - Conference contribution
SP - 111
EP - 114
BT - Proceedings of the 9th Workshop on Argument Mining
T2 - 9th Workshop on Argument Mining
Y2 - 17 October 2022 through 17 October 2022
ER -