Hierarchical feature-based translation for scalable natural language understanding

For complex natural language understanding systems with a large number of statistically confusable but semantically different for-mal commands, there are many difficulties in performing an accu-rate translation of a user input into a formal command in a single step. This paper addresses scalability issues in natural language understanding, and describes a method for performing the transla-tion in a hierarchical manner. The hierarchical method improves the system accuracy, reduces the computational complexity of the translation, provides additional numerical robustness during train-ing and decoding, and permits a more efficient packaging of the components of the natural language understanding system. 1.