A key advantage of our representation is that it has a structure similar to standard constituency parses, allowing us to easily adapt algorithms developed for phrase structure parsing for inference.
we use linear-time Recurrent Neural Network Grammars (RNNG) (Dyer et al., 2016) and show that the inductive bias provided by this model significantly improves the accuracy compared to strong sequence-to-sequence (seq2seq) models based on CNNs, LSTMs and Transformers.