End-to-end Modeling for Selection of Utterance Constructional Units via System Internal States

In order to make conversational agents or robots conduct human-like behaviors, it is important to design a model of the system internal states. In this paper, we address a model of favorable impression to the dialogue partner. The favorable impression is modeled to change according to user’s dialogue behaviors and also affect following dialogue behaviors of the system, specifically selection of utterance constructional units. For this modeling, we propose a hierarchical structure of logistic regression models. First, from the user’s dialogue behaviors, the model estimates the level of user’s favorable impression to the system and also the level of the user’s interest in the current topic. Then, based on the above results, the model predicts the system’s favorable impression to the user. Finally, the model determines selection of utterance constructional units in the next system turn. We train each of the logistic regression models individually with a small amount of annotated data of favorable impression. Afterward, the entire multi-layer network is fine-tuned with a larger amount of dialogue behavior data. An experimental result shows that the proposed method achieves higher accuracy on the selection of the utterance constructional units, compared with methods that do not take into account the system internal states.