This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment. In this special session, we will release human-annotated conversational data in three domains (movie-ticket booking, restaurant reservation, and taxi booking), as well as an experiment platform with built-in simulators in each domain, for training and evaluation purposes. The final submitted systems will be evaluated both in simulated setting and by human judges.

Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems.

See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons

In the war for talents, the need for appropriate tools to fill open positions with the right talents is becoming increasingly important for employers. AI-based technologies simplify recruiters’ daily work and increase the efficiency of the recruitment process by replacing time-consuming approaches. However, little is known about the reactions of stakeholders to AI-based recruiting. Thus, this paper aims to identify personal and contextual factors that influence the acceptance of AI-based technologies in the recruitment process. Based on the interviews with recruiters, managers, and applicants involved in the recruitment process, we present that transparency, complementary features of the AI tools, and a sense of control play key roles in the acceptance of AI-based technology when used for recruiting. The findings contribute to research on the adoption of AI in the recruitment process and provide recommendations on the use of AI technologies when hiring talents.

/pdf/computer-whom-should-i-hire-acceptance-criteria-for-4iqbm5rjvm.pdf

Computer, Whom Should I Hire? - Acceptance Criteria for Artificial Intelligence in the Recruitment Process.

Abstract Millions of people experience mental health issues each year, increasing the necessity for health-related services. One emerging technology with the potential to help address the resulting shortage in health care providers and other barriers to treatment access are conversational agents (CAs). CAs are software-based systems designed to interact with humans through natural language. However, CAs do not live up to their full potential yet because they are unable to capture dynamic human behavior to an adequate extent to provide responses tailored to users’ personalities. To address this problem, we conducted a design science research (DSR) project to design personality-adaptive conversational agents (PACAs). Following an iterative and multi-step approach, we derive and formulate six design principles for PACAs for the domain of mental health care. The results of our evaluation with psychologists and psychiatrists suggest that PACAs can be a promising source of mental health support. With our design principles, we contribute to the body of design knowledge for CAs and provide guidance for practitioners who intend to design PACAs. Instantiating the principles may improve interaction with users who seek support for mental health issues. 

/pdf/designing-personality-adaptive-conversational-agents-for-g8qpogjh.pdf

Designing Personality-Adaptive Conversational Agents for Mental Health Care

Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive characteristics of design elements for chatbots to facilitate development, adoption, implementation, and further research. To close this gap, the paper outlines a taxonomy of design elements for chatbots with 17 dimensions organized into the perspectives intelligence, interaction and context. The conceptually grounded design elements of the taxonomy are used to analyze 103 chatbots from 23 different application domains. Through a clustering-based approach, five chatbot archetypes that currently exist for domain-specific chatbots are identified. The developed taxonomy provides a structure to differentiate and categorize domain-specific chatbots according to archetypal qualities that guide practitioners when taking design decisions. Moreover, the taxonomy serves academics as a foundation for conducting further research on chatbot design while integrating scientific and practical knowledge.

https://link.springer.com/content/pdf/10.1007/s12599-020-00644-1.pdf

Virtual Assistance in Any Context

/pdf/a-matter-of-trust-examination-of-chatbot-usage-in-insurance-3k18fmd21z.pdf

A Matter of Trust? Examination of Chatbot Usage in Insurance Business

Chatbots are becoming increasingly important in the customer service sector due to their service automation, cost saving opportunities and broad customer satisfaction Similarly, in the business-to-business (B2B) sector, more and more companies use chatbots on their websites and social media channels, to establish sales team contact, to provide information about their products and services or to help customers with their requests and claims Customer relations in the B2B environment are especially characterized by a high level of personal contact service and support through expert explanations due to the complexity of the products and service offerings In order to support these efforts, chatbots can be used to assist buying centers along the purchase decision process However, B2B chatbots have so far only been marginally addressed in the scientific human-computer interaction and information systems literature To provide both researchers and practitioners with knowledge about the characteristics and archetypal patterns of chatbots currently existing in B2B customer services, we develop and discuss a 17-dimensional chatbot taxonomy for B2B customer services based on Nickerson et al [1] By classifying 40 chatbots in a cluster analysis, this study has identified three archetypal structures prevailing in B2B customer service chatbot usage

More than FAQ! Chatbot Taxonomy for Business-to-Business Customer Services

Many organizations are pursuing the implementation of chatbots to enable automation of service processes. However, previous research has highlighted the existence of practical setbacks in the implementation of chatbots in corporate environments. To gain practical insights on the issues related to the implementation processes from several perspectives and stages of deployment, we conducted semi-structured interviews with developers and experts of chatbot development. Using qualitative content analysis and based on a review of literature on human computer interaction (HCI), information systems (IS), and chatbots, we present an implementation framework that supports the successful deployment of chatbots and discuss the implementation of chatbots through a user-oriented lens. The proposed framework contains 101 guiding questions to support chatbot implementation in an eight-step process. The questions are structured according to the people, activity, context, and technology (PACT) framework. The adapted PACT framework is evaluated through expert interviews and a focus group discussion (FGD) and is further applied in a case study. The framework can be seen as a bridge between science and practice that serves as a notional structure for practitioners to introduce a chatbot in a structured and user-oriented manner. 

Antje Henriette Annette Janssen

Papers

Virtual Assistance in Any Context

See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons

A Matter of Trust? Examination of Chatbot Usage in Insurance Business

More than FAQ! Chatbot Taxonomy for Business-to-Business Customer Services

How to Make chatbots productive - A user-oriented implementation framework