Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
Jiangming Liu, Shay B. Cohen, Mirella Lapata · 2019
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-tosequence 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 2.2.0).Our best system achieves a score of 84.8% F 1 in the DRS parsing shared task.