  When developing a conversational agent, there is often an urgent need to have
a prototype available in order to test the application with real users. A
Wizard of Oz is a possibility, but sometimes the agent should be simply
deployed in the environment where it will be used. Here, the agent should be
able to capture as many interactions as possible and to understand how people
react to failure. In this paper, we focus on the rapid development of a natural
language understanding module by non experts. Our approach follows the learning
paradigm and sees the process of understanding natural language as a
classification problem. We test our module with a conversational agent that
answers questions in the art domain. Moreover, we show how our approach can be
used by a natural language interface to a cinema database.
