Simple and Effective Graph-to-Graph Annotation Conversion

Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che


Abstract
Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour. Previous work has been limited in conversion between tree-structured datasets and mainly focused on feature-based models which are not easily applicable to new conversions. In this paper, we propose two simple and effective graph-to-graph annotation conversion approaches, namely Label Switching and Graph2Graph Linear Transformation, which use pseudo data and inherit parameters to guide graph conversions respectively. These methods are able to deal with conversion between graph-structured annotations and require no manually designed features. To verify their effectiveness, we manually construct a graph-structured parallel annotated dataset and evaluate the proposed approaches on it as well as other existing parallel annotated datasets. Experimental results show that the proposed approaches outperform strong baselines with higher conversion score. To further validate the quality of converted graphs, we utilize them to train the target parser and find graphs generated by our approaches lead to higher parsing score than those generated by the baselines.
Anthology ID:
2022.coling-1.484
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5450–5460
Language:
URL:
https://aclanthology.org/2022.coling-1.484
DOI:
Bibkey:
Cite (ACL):
Yuxuan Wang, Zhilin Lei, Yuqiu Ji, and Wanxiang Che. 2022. Simple and Effective Graph-to-Graph Annotation Conversion. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5450–5460, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Simple and Effective Graph-to-Graph Annotation Conversion (Wang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.484.pdf
Code
 wangyuxuan93/g2gconversion