At test time, these models require a relatively expensive recognition algo-rithm (Collins, 1999;Titov and Henderson, 2007) to recover the parse tree, but the parsing performance consistently lags behind their discriminative competitors (Nivre et al., 2007;Huang, 2008;Goldberg and Elhadad, 2010), which are directly trained to maximize the conditional probability of the parse tree given the sentence, where linear-time decoding algorithms exist (e.g., for transition-based parsers).
we use the following features: 1) the stack embedding d t which encodes the stack of the decoder and is obtained with a stack-LSTM (Dyer et al., 2015(Dyer et al., , 2016; 2) the output buffer embedding o t ; we use a standard LSTM to compose the output buffer and o t is represented as the most recent state of the LSTM; and 3) the parent non-terminal embedding n t which is accessible in the generative model because the RNNG employs a depth-first generation order.