Combination of statistical and rule-based approaches for spoken language understanding

A Natural User Interface (NUI), where a user can type or speak a request, is a good complement to the well-known Graphical User Interface (GUI). Accurately extracting user intent from such typed or spoken queries is a very difficult challenge. In this paper we evaluate several techniques to extract user intent from typed sentences in the context of the well-known Airline Travel Information (ATIS) domain, where we want to extract which of the possible tasks the user wants to do and the value of the slots associated to that task. In previous work we showed that a Semantic Context Free Grammar (CFG) semi-automatically derived from labeled data can offer very good results. In this paper we evaluate several statistical pattern recognition techniques including Support Vector Machines (SVM), Naive Bayes classifiers and task-dependent n-gram language models. These methods can yield a very low task classification error rate. If used in combination with our CFG system, they can also lead to very low slot error rates.