Abstract
Sequential recurrent neural networks have
achieved superior performance on language
modeling, but overlook the structure information in natural language. Recent works on
structure-aware models have shown promising results on language modeling. However,
how to incorporate structure knowledge on
corpus without syntactic annotations remains
an open problem. In this work, we propose
neural variational language model (NVLM),
which enables the sharing of grammar knowledge among different corpora. Experimental results demonstrate the effectiveness of
our framework on two popular benchmark
datasets. With the help of shared grammar, our
language model converges significantly faster
to a lower perplexity on new training corpus.