Abstract
This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intense approaches, it clearly outperforms other models that are also trained without additional linguistic features.- Anthology ID:
- K19-1072
- Volume:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Mohit Bansal, Aline Villavicencio
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 768–777
- Language:
- URL:
- https://aclanthology.org/K19-1072
- DOI:
- 10.18653/v1/K19-1072
- Cite (ACL):
- René Knaebel, Manfred Stede, and Sebastian Stober. 2019. Window-Based Neural Tagging for Shallow Discourse Argument Labeling. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 768–777, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Window-Based Neural Tagging for Shallow Discourse Argument Labeling (Knaebel et al., CoNLL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/K19-1072.pdf