Abstract
Argumentative structure prediction aims to establish links between textual units and label the relationship between them, forming a structured representation for a given input text. The former task, linking, has been identified by earlier works as particularly challenging, as it requires finding the most appropriate structure out of a very large search space of possible link combinations. In this paper, we improve a state-of-the-art linking model by using multi-task and multi-corpora training strategies. Our auxiliary tasks help the model to learn the role of each sentence in the argumentative structure. Combining multi-corpora training with a selective sampling strategy increases the training data size while ensuring that the model still learns the desired target distribution well. Experiments on essays written by English-as-a-foreign-language learners show that both strategies significantly improve the model’s performance; for instance, we observe a 15.8% increase in the F1-macro for individual link predictions.- Anthology ID:
- 2021.argmining-1.2
- Volume:
- Proceedings of the 8th Workshop on Argument Mining
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–23
- Language:
- URL:
- https://aclanthology.org/2021.argmining-1.2
- DOI:
- 10.18653/v1/2021.argmining-1.2
- Cite (ACL):
- Jan Wira Gotama Putra, Simone Teufel, and Takenobu Tokunaga. 2021. Multi-task and Multi-corpora Training Strategies to Enhance Argumentative Sentence Linking Performance. In Proceedings of the 8th Workshop on Argument Mining, pages 12–23, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Multi-task and Multi-corpora Training Strategies to Enhance Argumentative Sentence Linking Performance (Putra et al., ArgMining 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.argmining-1.2.pdf
- Code
- wiragotama/argmin2021