The questions "How human-like is this dialogue robot?" and "How natural was the conversation with this dialogue robot?" are major concerns for dialogue robot researchers and developers. However, they have overlooked the way that unique conversational structures exist in actual conversations between humans and dialogue robots, which are different from those between humans. In this paper, we focus on the repetition of the user's own speech, and the user's commenting in the absence of a robot's response, in a conversation with a dialogue robot. These phenomena are unique to conversations with dialogue robots. When the user's speech is not inputted into dialogue robots, users often repeat their own speech. In addition, when the repeated speech is also not inputted to the dialogue robot, users often comment on the absence of response from the robot by giving reasons why the robot does not respond. These phenomena are organized in order, which means the repetition is performed firstly, and if the repeated speech is not inputted, then secondly, users will comment on the absence of response from the robot. We analyze these situations using conversation analysis methods, and discuss how these phenomena are organized in order, and how these phenomena are unique to conversations with dialogue robots. In the last part of the paper, we reconsider the "human-likeness" of dialogue robots.

There are several problems in applying grammatical error correction (GEC) to a writing support system. One of them is the handling of sentences in the middle of the input. Till date, the performance of GEC for incomplete sentences is not well-known. Hence, we analyze the performance of GEC model for incomplete sentences. Another problem is the correction speed. When the speed is slow, the usability of the system is limited, and the user experience is degraded. Therefore, in this study, we also focus on the non-autoregressive (NAR) model, which is a widely studied fast decoding method. We perform GEC in Japanese with traditional autoregressive and recent NAR models and analyze their accuracy and speed. Furthermore, in this study, we construct a writing support system with a grammatical error correction function. Specifically, the trained NAR model is embedded in the back-end system. We confirm the system’s effectiveness by both objective and subjective evaluations.

In this paper, we propose a new SEIR model for COVID-19 infection prediction using mobile statistics and evolutionally optimisation, which takes into account the risk of influx. The model is able to predict the number of infected people in a region with high accuracy, and the results of estimation in Sapporo City and Tokyo Metropolitan show high prediction accuracy. Using this model, we analyse the impact of the risk of influx to Sapporo City and show that the spread of infection in November could have been reduced to 0.6 if the number of influxes had been limited after the summer. We also examine the preventive measures called for in the emergency declaration in the Tokyo metropolitan area. We found that comprehensive measures are highly effective, and estimated the effect of vaccination and circuit breakers on the spread of infection after the spring of 2021 using the effective reproduction reduction rate of infection control measures obtained from the individual-based model and the SEIR model.