1. A method of monitoring natural language understanding of a user's input communication in a task classification system that operates on a task objective of a user, the method comprising a) if the user's input communication is sufficiently free of natural language understanding (NLU) errors that the user's input communication can be understood, then making a task classification decision, and b) if the user's input communication contains NLU errors that make the user's input communication not able to be understood, then i) if a probability of understanding the user's input communication exceeds a first threshold, conducting further dialog with the user, ii) otherwise, routing the user to a human for further assistance.
1. A method of monitoring natural language understanding of a user's input communication in a task classification system that operates on a task objective of a user, the method comprising a) if the user's input communication is sufficiently free of natural language understanding (NLU) errors that the user's input communication can be understood, then making a task classification decision, and b) if the user's input communication contains NLU errors that make the user's input communication not able to be understood, then i) if a probability of understanding the user's input communication exceeds a first threshold, conducting further dialog with the user, ii) otherwise, routing the user to a human for further assistance. 6. The method of claim 1 wherein the probability is determined using training data stored in a training database, the training data including at least one of classification models and extracted features.