Abstract
This paper proposes a classification model for single label implicit discourse relation recognition trained on soft-label distributions. It follows the PDTB 3.0 framework and it was trained and tested on the DiscoGeM corpus, where it achieves an F1-score of 51.38 on third-level sense classification of implicit discourse relations. We argue that training on soft-label distributions allows the model to better discern between more ambiguous discourse relations.- Anthology ID:
- 2024.codi-1.11
- Volume:
- Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Michael Strube, Chloe Braud, Christian Hardmeier, Junyi Jessy Li, Sharid Loaiciga, Amir Zeldes, Chuyuan Li
- Venues:
- CODI | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 120–126
- Language:
- URL:
- https://aclanthology.org/2024.codi-1.11
- DOI:
- Cite (ACL):
- Nelson Filipe Costa and Leila Kosseim. 2024. Exploring Soft-Label Training for Implicit Discourse Relation Recognition. In Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024), pages 120–126, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Soft-Label Training for Implicit Discourse Relation Recognition (Costa & Kosseim, CODI-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2024.codi-1.11.pdf