|Michimasa Inaba and Kenichi Takahashi|
In this study, we present our neural utterance ranking (NUR) model, an utterance selection model for conversational dialogue agents. The NUR model ranks candidate utterances with respect to their suitability in relation to a given context using neural networks; in addition, a dialogue system based on the model con-verses with humans using highly ranked utterances. Specifically, the model processes word sequences in utterances and utterance sequences in context via recurrent neural networks. Experimental results show that the proposed model ranks utterances with higher precision relative to deep learning and other existing methods. Furthermore, we construct a conversational dialogue system based on the proposed method and conduct experiments on human subjects to evaluate performance. The experimental result indicates that our system can offer a response that does not provoke a critical dialogue breakdown with a probability of 92% and a very natural response with a probability of 58%.