What Do Recurrent Neural Network Grammars Learn About Syntax?
Table 1: Phrase-structure parsing performance on PTB §23. All results are reported using single-model performance and without any additional data.
['[BOLD] Model', '[ITALIC] F1']
\newcitevinyals:2015 directly predict the sequence of nonterminals, “shifts” (which consume a terminal symbol), and parentheses from left to right, conditional on the input terminal sequence x, while \newcitechoe:2016 used a sequential LSTM language model on the same linearized trees to create a generative variant of the \newcitevinyals:2015 model. The generative model is used to re-rank parse candidates. Beyond this, Choe and Charniak’s generative variant of Vinyals et al. (2015) is another instance where generative models trained on the PTB outperform discriminative models.