  As a language model that integrates traditional symbolic operations and
flexible neural representations, recurrent neural network grammars (RNNGs) have
attracted great attention from both scientific and engineering perspectives.
However, RNNGs are known to be harder to scale due to the difficulty of batched
training. In this paper, we propose effective batching for RNNGs, where every
operation is computed in parallel with tensors across multiple sentences. Our
PyTorch implementation effectively employs a GPU and achieves x6 speedup
compared to the existing C++ DyNet implementation with model-independent
auto-batching. Moreover, our batched RNNG also accelerates inference and
achieves x20-150 speedup for beam search depending on beam sizes. Finally, we
evaluate syntactic generalization performance of the scaled RNNG against the
LSTM baseline, based on the large training data of 100M tokens from English
Wikipedia and the broad-coverage targeted syntactic evaluation benchmark. Our
RNNG implementation is available at https://github.com/aistairc/rnng-pytorch/.
