  In language modeling, it is difficult to incorporate entity relationships
from a knowledge-base. One solution is to use a reranker trained with global
features, in which global features are derived from n-best lists. However,
training such a reranker requires manually annotated n-best lists, which is
expensive to obtain. We propose a method based on the contrastive estimation
method that alleviates the need for such data. Experiments in the music domain
demonstrate that global features, as well as features extracted from an
external knowledge-base, can be incorporated into our reranker. Our final
model, a simple ensemble of a language model and reranker, achieves a 0.44\%
absolute word error rate improvement over an LSTM language model on the blind
test data.
