Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications. Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets. To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU. We evaluate the

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... ctor by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences. The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs. In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU. Thus, we confirmed our method offers advantages in SLU performances for small training data sets.