Chatbots

Paper session

会議の名前
CHI 2020
A Conversation Analysis of Non-Progress and Coping Strategies with a Banking Task-Oriented Chatbot
要旨

Task-oriented chatbots are becoming popular alternatives for fulfilling users' needs, but few studies have investigated how users cope with conversational 'non-progress' (NP) in their daily lives. Accordingly, we analyzed a three-month conversation log between 1,685 users and a task-oriented banking chatbot. In this data, we observed 12 types of conversational NP; five types of content that was unexpected and challenging for the chatbot to recognize; and 10 types of coping strategies. Moreover, we identified specific relationships between NP types and strategies, as well as signs that users were about to abandon the chatbot, including 1) three consecutive incidences of NP, 2) consecutive use of message reformulation or switching subjects, and 3) using message reformulation as the final strategy. Based on these findings, we provide design recommendations for task-oriented chatbots, aimed at reducing NP, guiding users through such NP, and improving user experiences to reduce the cessation of chatbot use.

キーワード
chatbot
conversation analysis
breakdowns
non-progress
coping strategies
著者
Chi-Hsun Li
National Chiao Tung University, Hsinchu, Taiwan Roc
Su-Fang Yeh
National Chiao Tung University, Hsinchu, Taiwan Roc
Tang-Jie Chang
National Chiao Tung University, Hsinchu, Taiwan Roc
Meng-Hsuan Tsai
National Chiao Tung University, Hsinchu, Taiwan Roc
Ken Chen
National Chiao Tung University, Hsinchu, Taiwan Roc
Yung-Ju Chang
National Chiao Tung University, Hsinchu, Taiwan Roc
DOI

10.1145/3313831.3376209

論文URL

https://doi.org/10.1145/3313831.3376209

It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community
要旨

While the majority of research in chatbot design has focused on creating chatbots that engage with users one-on-one, less work has focused on the design of conversational agents for online communities. In this paper we present results from a three week test of a social chatbot in an established online community. During this study, the chatbot "grew up" from "birth" through its teenage years, engaging with community members and "learning" vocabulary from their conversations. We discuss the design of this chatbot, how users' interactions with it evolved over the course of the study, and how it impacted the community as a whole. We discuss how we addressed challenges in developing a chatbot whose vocabulary could be shaped by users, and conclude with implications for the role of machine learning in social interactions in online communities and potential future directions for design of community-based chatbots.

キーワード
chatbot
interaction design
machine learning
AI
BabyBot
Twitch
community interaction
long-term study
著者
Joseph Seering
Carnegie Mellon University, Pittsburgh, PA, USA
Michal Luria
Carnegie Mellon University, Pittsburgh, PA, USA
Connie Ye
Carnegie Mellon University, Pittsburgh, PA, USA
Geoff Kaufman
Carnegie Mellon University, Pittsburgh, PA, USA
Jessica Hammer
Carnegie Mellon University, Pittsburgh, PA, USA
DOI

10.1145/3313831.3376708

論文URL

https://doi.org/10.1145/3313831.3376708

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills
要旨

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills—the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

キーワード
Conversational Agents
AI chatbot
Active Listening
Interview Chatbot
Chatbot Platform
Deep Learning
著者
Ziang Xiao
University of Illinois at Urbana-Champaign, Urbana, IL, USA
Michelle X. Zhou
Juji, Inc., San Jose, CA, USA
Wenxi Chen
Juji, Inc., San Jose, CA, USA
Huahai Yang
Juji. Inc., San Jose, CA, USA
Changyan Chi
Juji. Inc., Beijing, China
DOI

10.1145/3313831.3376131

論文URL

https://doi.org/10.1145/3313831.3376131

動画
Bot in the Bunch: Facilitating Discussion in Group Chat by Improving Efficiency and Participation with a Chatbot
要旨

Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.

キーワード
Chatbot
Conversational agent
Group chat
Discussion
Consensus
Online communication
著者
Soomin Kim
Seoul National University, Seoul, Republic of Korea
Jinsu Eun
Seoul National University, Seoul, Republic of Korea
Changhoon Oh
Carnegie Mellon University, Pittsburgh, PA, USA
Bongwon Suh
Seoul National University, Seoul, Republic of Korea
Joonhwan Lee
Seoul National University, Seoul, Republic of Korea
DOI

10.1145/3313831.3376785

論文URL

https://doi.org/10.1145/3313831.3376785

"I Hear You, I Feel You": Encouraging Deep Self-disclosure through a Chatbot
要旨

Chatbots have great potential to serve as a low-cost, effective tool to support people's self-disclosure. Prior work has shown that reciprocity occurs in human-machine dialog; however, whether reciprocity can be leveraged to promote and sustain deep self-disclosure over time has not been systematically studied. In this work, we design, implement and evaluate a chatbot that has self-disclosure features when it performs small talk with people. We ran a study with 47 participants and divided them into three groups to use different chatting styles of the chatbot for three weeks. We found that chatbot self-disclosure had a reciprocal effect on promoting deeper participant self-disclosure that lasted over the study period, in which the other chat styles without self-disclosure features failed to deliver. Chatbot self-disclosure also had a positive effect on improving participants' perceived intimacy and enjoyment over the study period. Finally, we reflect on the design implications of chatbots where deep self-disclosure is needed over time.

キーワード
Conversation
Chatbot
Self-disclosure, Mental well-being
著者
Yi-Chieh Lee
University of Illinois at Urbana-Champaign & NTT Japan, Champaign, IL, USA
Naomi Yamashita
NTT Japan, Keihanna, Japan
Yun Huang
University of Illinois at Urbana-Champaign, Champaign, IL, USA
Wai Fu
University of Illinois at Urbana-Champaign, Champaign, IL, USA
DOI

10.1145/3313831.3376175

論文URL

https://doi.org/10.1145/3313831.3376175