The current chat dialogue systems implicitly consider the topic given the context, but not explicitly. As a result, these systems often generate inconsistent responses with the topic of the moment. In this study, we propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality.” In topicality estimation, the model is trained through self-supervised learning that regards entities that appear in both context and response as the topic entities. In response generation, the model is trained to generate topic-relevant responses based on the estimated topicality. Experimental results show that our proposed system can follow the topic more than the existing dialogue system that considers only the context.

Interview is an efficient way to elicit knowledge from experts of different domains. In this paper, we introduce CIDC, an interview dialogue corpus in the culinary domain in which interviewers play an active role to elicit culinary knowledge from the cooking expert. The corpus consists of 308 interview dialogues (each about 13 minutes in length), which add up to a total of 69,000 utterances. We use a video conferencing tool for data collection, which allows us to obtain the facial expressions of the interlocutors as well as the screen-sharing contents. To understand the impact of the interlocutors’ skill level, we divide the experts into “semi-professionals’” and “enthusiasts” and the interviewers into “skilled interviewers” and “unskilled interviewers.” For quantitative analysis, we report the statistics and the results of the post-interview questionnaire. We also conduct qualitative analysis on the collected interview dialogues and summarize the salient patterns of how interviewers elicit knowledge from the experts. The corpus serves the purpose to facilitate future research on the knowledge elicitation mechanism in interview dialogues.

We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations. However, there is no dialogue dataset with structured external knowledge designed to make detailed recommendations for the target. Therefore, we construct a dialogue dataset, Japanese Movie Recommendation Dialogue (JMRD), in which the recommender recommends one movie in a long dialogue (23 turns on average). The external knowledge used in this dataset is hierarchically structured, including title, casts, reviews, and plots. Every recommender’s utterance is associated with the external knowledge related to the utterance. We then create a movie recommendation dialogue system that considers the structure of the external knowledge and the history of the knowledge used. Experimental results show that the proposed model is superior in knowledge selection to the baseline models.

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.