@inproceedings{taslimipoor-etal-2019-gcn,
    title = "{GCN}-Sem at {S}em{E}val-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks",
    author = "Taslimipoor, Shiva  and
      Rohanian, Omid  and
      Mo{\v{z}}e, Sara",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/S19-2014/",
    doi = "10.18653/v1/S19-2014",
    pages = "102--106",
    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."
}