Window-Based Neural Tagging for Shallow Discourse Argument Labeling

René Knaebel, Manfred Stede, Sebastian Stober


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
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/K19-1072.pdf