出版社：The Japanese Society for Artificial Intelligence
摘要：To design a dialogue well and easily implement it into an agent dialogue system, we constructed and verified typical dialogue patterns as “ Dialogue Design Patterns ”. To analyze the dialogues, we defined the “ Standard Dialogue Structure ”that represents simple components of dialogues. This is constructed using the“ Turn ”that is one turn-taking between the dialogue participants, the“ Topic ”that is a series of Turns in which a single question is resolved, the“Topic-shift”that is short interaction in which a consensus is built before moving to the next Topic, and the“ Scene ”that puts together each component. We investigated the frequent patterns of dialogue in the Topic. We analyzed the content of each Turn’s utterances on two axes:“convey-receive”and“logical-emotional”. These Turns were labeled on a four-quadrant impression as“ teach ”(logical convey),“ empathy ”(emotional convey),“ survey ” (logical receive), and“ listen ”(emotional receive). We analyzed the frequency of appearance for these labels in the Turns for each Topic using frequent pattern analysis, and we found several typical patterns that order these labels. We named these the patterns and determined their tendency scores. The tendencies of these patterns were categorized into“ information amount ”,“ cooperation ”, and“ balance ”. Finally, we implemented a dialogue scenario created using the Standard Dialogue Structures and Dialogue Design Patterns in our system. In the feasibility study with local government employees, the achievement rate of agent dialogue was more than 50%, and it higher than our previous system. This shows that dialogues based on Dialogue Design Patterns can facilitate agent dialogue. In conclusion, we showed that applying the Standard Dialogue Structure and Dialogue Design Patterns to dialogues in a human-agent dialogue system is a practical and possible way of easily implementing effective dialogues that facilitate interaction even for novices in dialogue design.
关键词：topic-shift;selection-based dialogue;pattern mining;pattern tendency;evaluation score;proof of concept
