GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks

Shiva Taslimipoor, Omid Rohanian, Sara Može


Abstract
This paper describes the system submitted to the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with UCCA’. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.
Anthology ID:
S19-2014
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
102–106
Language:
URL:
https://aclanthology.org/S19-2014
DOI:
10.18653/v1/S19-2014
Bibkey:
Cite (ACL):
Shiva Taslimipoor, Omid Rohanian, and Sara Može. 2019. GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 102–106, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks (Taslimipoor et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2014.pdf