Effects of Conversational Agents on Activation of Communication in Thought-Evoking Multi-Party Dialogues

Abstract

SUMMARYThis paper presents an experimental study that analyzes how conversational agents activate human communication in thoughtevoking multi-party dialogues between multi-users and multi-agents. A thought-evoking dialogue is a kind of interaction in which agents act to provoke user thinking, and it has the potential to activate multi-party interactions. This paper focuses on quiz-style multi-party dialogues between two users and two agents as an example of thought-evoking multi-party dialogues. The experimental results revealed that the presence of a peer agent significantly improved user satisfaction and increased the number of user utterances in quiz-style multi-party dialogues. We also found that agents' empathic expressions significantly improved user satisfaction, improved user ratings of the peer agent, and increased the number of user utterances. Our findings should be useful for activating multi-party communications in various applications such as pedagogical agents and community facilitators. key words: multi-party interaction, dialogue systems, human-agent interaction, human-robot interaction

How should my chatbot interact? A survey on human-chatbot interaction design

“…In addition, the empathic expressions also improved the user ratings of the peer agent regarding intimacy, compassion, amiability, and encouragement. Although [Dohsaka et al 2014] did not find effect of chatbot's self-disclosure on emotional connection, [Lee and Choi 2017] found that self-disclosure and reciprocity significantly improved trust and interactional enjoyment. In [Fitzpatrick et al 2017], seven participants reported that the best thing about their experience with the therapist chatbot was the perceived empathy.…”

“…We identified 13 studies that report emotional intelligence. Unlikely the previously discussed categories, most studies on emotional intelligence focused on understanding the effects of chatbots' empathy and emotional self-disclosure [Ayedoun et al 2017;Dohsaka et al 2014;Fitzpatrick et al 2017;Ho et al 2018;Kumar et al 2010;Lee and Choi 2017;Miner et al 2016;Morris 2002;Portela and Granell-Canut 2017;Shum et al 2018]. Only three papers highlighted emotional intelligence as an exploratory outcome [Jenkins et al 2007;Thies et al 2017;Zamora 2017], where needs for emotional intelligence emerged from participants subjective feedback and post-intervention surveys.…”

“…According to [Wallis and Norling 2005], when a chatbot's emotional reaction triggers a social response from the user, then the chatbot had achieved group membership and the users' sympathy. [Dohsaka et al 2014] proposed a chatbot that uses empathic and self-oriented emotional expressions to keep users engaged in quiz-style dialog. The survey results revealed that empathic expressions significantly improved user satisfaction.…”

“…Therefore, the authors defined conversation-turns per session as a success metric for chatbots, where usefulness and emotional understanding are combined. In [Dohsaka et al 2014], empathic utterances for the quiz-style interaction significantly increase the number of users' messages per hint for both answers and non-answers utterances (such as feedback about the success/failure). This result shows that empathic utterances encouraged the users to engage and utter non-answers statements.…”

“…[S1] using social-emotional utterances: affective utterances toward the user are a common strategy to demonstrate emotional intelligence. [Ayedoun et al 2017], [Kumar et al 2010], and [Dohsaka et al 2014] suggest that affective utterances improve the interpersonal relationship with a tutor chatbot. In [Ayedoun et al 2017], the authors propose affective backchannel utterances (congratulatory, encouraging, sympathetic, and reassuring) to motivate the user to communicate in a second language.…”