Context-sensitive language modeling for large sets of proper nouns in multimodal dialogue systems

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.