Hidden understanding models for statistical sentence understanding

We describe the first sentence understanding system that is completely based on learned methods both for understanding individual sentences, and determining their meaning in the context of preceding sentences. We divide the problem into three stages: semantic parsing, semantic classification, and discourse modeling. Each of these stages requires a different model. When we ran this system on the last test (December, 1994) of the ARPA Air Travel Information System (ATIS) task, we achieved a 13.7% error rate. The error rate for those sentences that are context-independent (class A) was 9.7%.