GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

Yue Yu, Yilun Zhu, Yang Liu, Yan Liu, Siyao Peng, Mackenzie Gong, Amir Zeldes


Abstract
In this paper we present GumDrop, Georgetown University’s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.
Anthology ID:
W19-2717
Volume:
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019
Month:
June
Year:
2019
Address:
Minneapolis, MN
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
133–143
Language:
URL:
https://aclanthology.org/W19-2717
DOI:
10.18653/v1/W19-2717
Bibkey:
Cite (ACL):
Yue Yu, Yilun Zhu, Yang Liu, Yan Liu, Siyao Peng, Mackenzie Gong, and Amir Zeldes. 2019. GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection. In Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019, pages 133–143, Minneapolis, MN. Association for Computational Linguistics.
Cite (Informal):
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection (Yu et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2717.pdf
Code
 gucorpling/GumDrop
Data
DISRPT2019