Abstract
Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results.- Anthology ID:
- 2021.bea-1.22
- Volume:
- Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 210–222
- Language:
- URL:
- https://aclanthology.org/2021.bea-1.22
- DOI:
- Cite (ACL):
- Tariq Alhindi and Debanjan Ghosh. 2021. “Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 210–222, Online. Association for Computational Linguistics.
- Cite (Informal):
- “Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays (Alhindi & Ghosh, BEA 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.bea-1.22.pdf