  In this work, we propose a new language modeling paradigm that has the
ability to perform both prediction and moderation of information flow at
multiple granularities: neural lattice language models. These models construct
a lattice of possible paths through a sentence and marginalize across this
lattice to calculate sequence probabilities or optimize parameters. This
approach allows us to seamlessly incorporate linguistic intuitions - including
polysemy and existence of multi-word lexical items - into our language model.
Experiments on multiple language modeling tasks show that English neural
lattice language models that utilize polysemous embeddings are able to improve
perplexity by 9.95% relative to a word-level baseline, and that a Chinese model
that handles multi-character tokens is able to improve perplexity by 20.94%
relative to a character-level baseline.
