Constructing a Non-task-oriented Dialogue Agent using Statistical Response Method and Gamification
Michimasa Inaba, Naoyuki Iwata, Fujio Toriumi, Takatsugu Hirayama, Yu Enokibori, Keníchi Takahashi, Kenji Mase · 2014
This paper provides a novel method for building non-task-oriented dialogue agents such as chatbots. The dialogue agent constructed using our method automatically selects a suitable utterance depending on a context from a set of candidate utterances prepared in advance. To realize automatic utterance selection, we rank the candidate utterances in order of suitability by application of a machine learning algorithm. We employed both right and wrong dialogue data to learn relative suitability to rank the utterances. Additionally, we provide a low-cost and quality-assured learning data acquisition environment using crowdsourcing and gamification. The results of an experiment using learning data obtained via the environment demonstrate that the appropriate utterance is ranked on the top in 82.6% of cases and within the top 3 at 95.0% of cases. Results show that using context information that is not used in most existing agents is necessary for appropriate responses.