Long-term chatbots are expected to develop relationships with users. The major trend in this field’s recent long-term chatbot studies is to train systems with virtual long-term chat data called Multi-Session Chat (MSC), which collects text chat from multiple sessions of crowd workers playing the roles of speakers with defined personas. However, no investigation has attempted to determine whether such virtual long-term chat can successfully simulate relationship-building between speakers. To clarify the difference between an actual long-term intimacy process and an MSC intimacy process, this study collects real long-term chat and MSC in Japanese and compares them in terms of speech form and dialogue acts. The results of analyzing these factors suggest that MSC have an unnatural tendency to behave as if they have a close relationship with non-polite speech levels compared to actual long-term chats, but also as if they have a shallow relationship with more questions than real long-term chats.

In the last few years, generative dialogue models have shown excellent performance and have been used for various applications. As chatbots become more prevalent in our daily lives, more and more people expect them to behave more like humans, but existing dialogue models do not consider the time information that people are constantly aware of. In this paper, we aim to construct a time-considerable dialogue model that actively utilizes time information. First, we categorize responses by their naturalness at different times and introduce a new metric to classify responses into our categories. Then, we propose a new reranking method to make the existing dialogue model time-considerable using the proposed metric and subjectively evaluate the performances of the obtained time-considerable dialogue models by humans.

Automation of dialogue system evaluation is a driving force for the efficient development of dialogue systems. This paper introduces the bipartite-play method, a dialogue collection method for automating dialogue system evaluation. It addresses the limitations of existing dialogue collection methods: (i) inability to compare with systems that are not publicly available, and (ii) vulnerability to cheating by intentionally selecting systems to be compared. Experimental results show that the automatic evaluation using the bipartite-play method mitigates these two drawbacks and correlates as strongly with human subjectivity as existing methods.

We address automatic citation sentence generation, which reduces the burden on writing scientific papers. For highly accurate citation senetence generation, appropriate language must be learned using information such as the relationship between the cited source and the cited paper as well as the context in which the paper cited. Although the abstracts of papers have been used for the generation in the past, they often contain extra information in the citation sentence, which might negatively impact the generation of citation sentences. Therefore, this study attempts to learn a highly accurate citation sentence generation model using sentences from cited articles that resemble the previous sentence to the cited location, thereby utilizing information that is more useful for citation sentence generation.

We are studying a cooperation style where multiple speakers can provide both advanced dialogue services and operator education. We focus on a style in which two operators interact with a user by pretending to be a single operator. For two operators to effectively act as one, each must adjust his/her conversational content and timing to the other. In the process, we expect each operator to experience the conversational content of his/her partner as if it were his/her own, creating efficient and effective learning of the other’s skill. We analyzed this educational effect and examined whether dialogue services can be successfully provided by collecting travel guidance dialogue data from operators who give travel information to users. In this paper, we report our preliminary results on dialogue content and user satisfaction of operators and users.