  In this paper we introduce Latent Tree Language Model (LTLM), a novel
approach to language modeling that encodes syntax and semantics of a given
sentence as a tree of word roles.
  The learning phase iteratively updates the trees by moving nodes according to
Gibbs sampling. We introduce two algorithms to infer a tree for a given
sentence. The first one is based on Gibbs sampling. It is fast, but does not
guarantee to find the most probable tree. The second one is based on dynamic
programming. It is slower, but guarantees to find the most probable tree. We
provide comparison of both algorithms.
  We combine LTLM with 4-gram Modified Kneser-Ney language model via linear
interpolation. Our experiments with English and Czech corpora show significant
perplexity reductions (up to 46% for English and 49% for Czech) compared with
standalone 4-gram Modified Kneser-Ney language model.
