GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
Shiva Taslimipoor | Omid Rohanian | Sara Može
Proceedings of the 13th International Workshop on Semantic Evaluation
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.
Cross-lingual Dependency Parsing of Related Languages with Rich Morphosyntactic Tagsets
Željko Agić | Jörg Tiedemann | Danijela Merkler | Simon Krek | Kaja Dobrovoljc | Sara Može
Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants
- Željko Agić 1
- Jörg Tiedemann 1
- Danijela Merkler 1
- Simon Krek 1
- Kaja Dobrovoljc 1
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