Recurrent Neural Network Grammars
Table 5: Parsing results with fixed composition function on PTB §23 (D=discriminative, G=generative, S=semisupervised). ⋆ indicates the (Vinyals et al., 2015) model trained only on the WSJ corpus without ensembling. † indicates RNNG models with the buggy composition function implementation.
['[BOLD] Model', '[BOLD] type', '[ITALIC]  [BOLD] F [BOLD] 1']
The implementation error affected both the generative and discriminative RNNGs. The considerable improvement in parsing accuracy indicates that properly composing the constituent and propagating information upwards is crucial. Despite slightly higher language modeling perplexity on PTB §23, the fixed RNNG still outperforms a highly optimized sequential LSTM baseline.