Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
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.
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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
DRS Parsing | PMB-2.2.0 | Transformer seq2seq | F1 | 87.1 | # 2 |