Francisco A. M. Valério
Raquel O. Prates
ABSTRACT
In this work, we used the results of applying the Semiotic Inspection Method (SIM) to popular chatbots[12, 13], which yielded eleven strategies for conveying features to users, as well as six sign classes used for designing their interaction. We conducted user studies comparing two prototype chatbots with same features and different approaches to interaction: one using Natural Language Processing, and other using the sign classes and strategies to guide the user interaction. After that, we interviewed the participants, asking about their preferred aspects of each chatbot and their opinions regarding some of these aspects, and, later, analyzed the results. These point to the effectiveness of the strategies and sign classes. Then, we discuss users' perceptions of different ways of interacting with chatbots and their communicative strategies.
- Daniel G. Bobrow. 1964. Natural Language Input for a Computer Problem Solving System. Technical Report. Massachusetts Institute of Technology, Cambridge, MA, USA. Google Scholar
- Heloisa Candello and Claudio Pinhanez. 2016. Designing Conversational Interfaces. In Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais - IHC 2016 (15th ed.). Vol. C - Livro dos Tutoriais. Sociedade Brasileira de Computação - SBC, Porto Alegre, RS, Brazil.Google Scholar
- Heloisa Candello, Claudio Pinhanez, and Flavio Figueiredo. 2017. Typefaces and the Perception of Humanness in Natural Language Chatbots. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 3476--3487. Google ScholarDigital Library
- Clarisse S. de Souza. 2005. The Semiotic Engineering of Human-computer Interaction. MIT Press.Google Scholar
- Thiago Carvalho D'Ávila. 2018. Kino: an approach for rule-based chatbot development, monitoring and evaluation. Master's thesis. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.Google Scholar
- Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N. Patel. 2018. Evaluating and Informing the Design of Chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18). ACM, New York, NY, USA. Google ScholarDigital Library
- Jiepu Jiang, Ahmed H. Awadallah, Rosie Jones, Umut Ozertem, Imed Zitouni, Ranjitha G. Kulkarni, and Omar Z. Khan. 2015. Automatic Online Evaluation of Intelligent Assistants. In Proceedings of the 24th International Conference on World Wide Web (WWW '15). ACM, New York, NY, USA, 506--516. Google ScholarDigital Library
- Michael McTear, Zoraida Callejas, and David Griol. 2016. Evaluating the Conversational Interface. In The Conversational Interface. Springer International Publishing, 379--402. Google ScholarDigital Library
- Michael F. McTear. 2017. The Rise of the Conversational Interface: A New Kid on the Block?. In Future and Emerging Trends in Language Technology. Machine Learning and Big Data (Lecture Notes in Computer Science), José F Quesada, Francisco-Jesús Martín Mateos, and Teresa López Soto (Eds.). Springer International Publishing, 38--49.Google Scholar
- Raquel Prates, Clarisse de Souza, and Simone Barbosa. 2000. A method for evaluating the communicability of user interfaces. Interactions 7 (01 2000), 31--38. Google ScholarDigital Library
- Francisco A. M. Valério. 2020. Chatbots and Communication: A Qualitative Analysis of Communicative Aspects of Conversational Interfaces. Master's thesis. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.Google Scholar
- Francisco A. M. Valério, Tatiane G. Guimarães, Raquel O. Prates, and Heloisa Candello. 2017. Here's What I Can Do: Chatbots' Strategies to Convey Their Features to Users. In Proceedings of the 16th Brazilian Symposium on Human Factors in Computer Systems (IHC '17). ACM, New York, NY, USA. Google ScholarDigital Library
- Francisco A. M. Valério, Tatiane G. Guimarães, Raquel O. Prates, and Heloisa Candello. 2018. Chatbots Explain Themselves: Designers' Strategies for Conveying Chatbot Features to Users. SBC Journal on Interactive Systems 9, 3 (Dec. 2018), 61--79. https://seer.ufrgs.br/jis/article/view/80272Google Scholar
- Joseph Weizenbaum. 1966. ELIZA --- A Computer Program for the Study of Natural Language Communication Between Man and Machine. Commun. ACM 9, 1 (Jan. 1966), 36--45. Google ScholarDigital Library
Index Terms
- Comparing users' perception of different chatbot interaction paradigms: a case study
Recommendations
Here's What I Can Do: Chatbots' Strategies to Convey Their Features to Users
IHC '17: Proceedings of the XVI Brazilian Symposium on Human Factors in Computing SystemsChatbots have been around since the 1960's, but recently they have risen in popularity especially due to new compatibility with social networks and messenger applications. Chatbots are different from traditional user interfaces, for they unveil ...
Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions
HAI '23: Proceedings of the 11th International Conference on Human-Agent InteractionChatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a ...
Is chatting with a sophisticated chatbot as good as chatting online or FTF with a stranger?
AbstractEmotionally-responsive chatbots are marketed as agents with which one can form emotional connections. They can also become weak ties in the outer layers of one's acquaintance network and available for social support. In this experiment,...
Highlights- Adults had positive reactions after talking to the emotionally responsive Replika chatbot.
Comments