@inproceedings{liu-etal-2019-discourse-representation,
    title = "Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    editor = "Abzianidze, Lasha  and
      van Noord, Rik  and
      Haagsma, Hessel  and
      Bos, Johan",
    booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-1203/",
    doi = "10.18653/v1/W19-1203",
    abstract = "We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8{\%} F1 in the DRS parsing shared task."
}