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We explore several language modeling strategies for increasing the recognition accuracy among large sets of proper nouns in a map- based multimodal dialogue system which provides restaurant information. In particular, we evaluate several mechanisms for exploiting dialogue context, the two most promising of which involve a semi- static metropolitan-region based large set of proper nouns competing with a smaller, in-focus subset. We show that these techniques decrease word, concept, and proper noun error rates under several training conditions. We also present a technique to generalize sparse training data through derived templates to improve language model robustness.