Multi-Task Learning Improves Performance in Deep Argument Mining Models
Amirhossein Farzam, Shashank Shekhar, Isaac Mehlhaff, Marco Morucci
Abstract
The successful analysis of argumentative techniques in user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, but each task is treated as separate and different bespoke models are fine-tuned for each dataset. We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining that meets or exceeds performance from existing methods for the same problems. Our model builds a shared representation of the input and exploits similarities between tasks in order to further boost performance via parameter-sharing. Our results are important for argument mining as they show that different tasks share substantial similarities and suggest a holistic approach to the extraction of argumentative techniques from text.- Anthology ID:
- 2024.argmining-1.5
- Volume:
- Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Yamen Ajjour, Roy Bar-Haim, Roxanne El Baff, Zhexiong Liu, Gabriella Skitalinskaya
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46–58
- Language:
- URL:
- https://aclanthology.org/2024.argmining-1.5
- DOI:
- Cite (ACL):
- Amirhossein Farzam, Shashank Shekhar, Isaac Mehlhaff, and Marco Morucci. 2024. Multi-Task Learning Improves Performance in Deep Argument Mining Models. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 46–58, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Learning Improves Performance in Deep Argument Mining Models (Farzam et al., ArgMining 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.argmining-1.5.pdf