Conversational Web

This paper lays the foundation for a new delivery paradigm for web-accessible content and functionality, i.e., conversational interaction. Instead of asking users to read text, click through links and type on the keyboard, the vision is to enable users to “speak to a website” and to obtain natural language, spoken feedback. The paper describes how state-of-the-art chatbot technology can enable a dialog between the user and the website, proposes a reference architecture for the automated inference of site-specific chatbots able to mediate between the user and the website, and discusses open challenges and research questions. The envisioned, bidirectional dialog paradigm advances current screen reader technology and aims to benefit both regular users in eyes-free usage scenarios as well as visually impaired users in everyday scenarios.

In this paper, we describe the foundations for generating a chatbot out of a website equipped with simple, bot-specific HTML annotations. The approach is part of what we call conversational web browsing, i.e., a dialog-based, natural language interaction with websites. The goal is to enable users to use content and functionality accessible through rendered UIs by “talking to websites” instead of by operating the graphical UI using keyboard and mouse. The chatbot mediates between the user and the website, operates its graphical UI on behalf of the user, and informs the user about the state of interaction. We describe the conceptual vocabulary and annotation format, the supporting conversational middleware and techniques, and the implementation of a demo able to deliver conversational web browsing experiences through Amazon Alexa.

Chatbots are software agents that are able to interact with humans in natural language. Their intuitive interaction paradigm is expected to significantly reshape the software landscape of tomorrow, while already today chatbots are invading a multitude of scenarios and contexts. This article takes a developer’s perspective, identifies a set of architectural patterns that capture different chatbot integration scenarios, and reviews state-of-the-art development aids.

In this paper we explore the opportunities brought by cognitive augmentation to provide a more natural and accessible web browsing experience. We explore these opportunities through conversational web browsing, an emerging interaction paradigm for the Web that enables blind and visually impaired users (BVIP), as well as regular users, to access the contents and features of websites through conversational agents. Informed by the literature, our previous work and prototyping exercises, we derive a conceptual framework for supporting BVIP conversational web browsing needs, to then focus on the challenges of automatically providing this support, describing our early work and prototype that leverage heuristics that consider structural and content features only.

Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

Conversational services are emerging as a new paradigm for accessing information by simply uttering questions in natural language, posing a whole new set of challenges to the design and engineering of information systems. Training conversational services to deal with the nuances of natural language often requires collecting a high-quality and diverse set of training samples (i.e., paraphrases). Traditional approaches such as hiring an expert or crowdsourcing involve data collection processes that are often costly and time-consuming. Automated paraphrase generation is a promising cost-effective and scalable approach to generating training samples. Current automatic techniques, however, tend to specialise in specific types of lexical or syntactic variations. As a result, generated paraphrases may not perform well in relevant quality aspects such as diversity and semantic relatedness. In this paper, we follow an approach inspired by services integration to address these issues and generate paraphrases in English that are semantically relevant and diverse. We propose an extensible and reusable pipeline that combines automatic paraphrasing techniques in a two-step process that first focus on i) leveraging the strengths of multiple techniques to generate the most diverse (and possibly noisy) set of paraphrases, to then ii) address common quality issues in a separate step. Through empirical evaluations we show the benefits of the two-step process design and of combining techniques for more balancing relevance and diversity.

Conversational AI is changing the way we interact with digital services. However, there is still a lack of conversational paradigms facilitating access to the Web. This paper discusses a new approach for Conversational Web Browsing, and introduces a design space identified through a user-centered process that involved 26 blind and visually impaired users. The paper also illustrates the conceptual architecture of a software framework that can automatically generate conversational agents for the Conversational Web.

Conversational AI is disrupting the way information is accessed. However, there is still a lack of conversational technologies leveraging the Web. This paper introduces an approach to support the notion of Conversational Web Browsing. It illustrates design patterns for navigating websites through conversation and shows how such patterns are sustained by a Web architecture that integrates NLP technologies.

Conversational agents are emerging as channels for a natural and accessible interaction with digital services. Their benefits span across a wide range of usage scenarios and address visual impairments and any situational impairments that may take advantage of voice-based interactions. A few works highlighted the potential and the feasibility of adopting conversational agents for making the Web truly accessible for everyone. Yet, there is still a lack of concrete guidance in designing conversational experiences for browsing the Web. This paper illustrates a human-centered process that involved 26 blind and visually impaired people to investigate their difficulties when using assistive technology for accessing the Web, and their attitudes and preferences on adopting conversational agents. In response to the identified challenges, the paper introduces patterns for conversational Web browsing. It also discusses design implications that can promote Conversational AI as a technology to enhance Web accessibility.