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In language modeling, it is difficult to incorporate entity relationshipsfrom a knowledge-base. One solution is to use a reranker trained with globalfeatures, in which global features are derived from n-best lists. However,training such a reranker requires manually annotated n-best lists, which isexpensive to obtain. We propose a method based on the contrastive estimationmethod that alleviates the need for such data. Experiments in the music domaindemonstrate that global features, as well as features extracted from anexternal knowledge-base, can be incorporated into our reranker. Our finalmodel, 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 blindtest data.

Entity-Aware Language Model as an Unsupervised Reranker