Multi-task Learning in Argument Mining for Persuasive Online Discussions

Nhat Tran, Diane Litman


Abstract
We utilize multi-task learning to improve argument mining in persuasive online discussions, in which both micro-level and macro-level argumentation must be taken into consideration. Our models learn to identify argument components and the relations between them at the same time. We also tackle the low-precision which arises from imbalanced relation data by experimenting with SMOTE and XGBoost. Our approaches improve over baselines that use the same pre-trained language model but process the argument component task and two relation tasks separately. Furthermore, our results suggest that the tasks to be incorporated into multi-task learning should be taken into consideration as using all relevant tasks does not always lead to the best performance.
Anthology ID:
2021.argmining-1.15
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ArgMining | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–153
Language:
URL:
https://aclanthology.org/2021.argmining-1.15
DOI:
10.18653/v1/2021.argmining-1.15
Bibkey:
Cite (ACL):
Nhat Tran and Diane Litman. 2021. Multi-task Learning in Argument Mining for Persuasive Online Discussions. In Proceedings of the 8th Workshop on Argument Mining, pages 148–153, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Multi-task Learning in Argument Mining for Persuasive Online Discussions (Tran & Litman, ArgMining 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.15.pdf