Language understanding (LU) modules for spoken dialogue systems in the early phases of their development need to be (i) easy to construct and (ii) robust against various expressions. Conventional methods of LU are not suitable for new domains, because they take a great deal of effort to make rules or transcribe and annotate a suf- _cient corpus for training. In our method, the weightings of the Weighted Finite State Transducer (WFST) are designed on two levels and simpler than those for conventional WFST-based methods. Therefore, our method needs much fewer training data, which enables rapid prototyping of LU modules. We evaluated our method in two different domains. The results revealed that our method outperformed baseline methods with less than one hundred utterances as training data, which can be reasonably prepared for new domains. This shows that our method is appropriate for rapid prototyping of LU modules.