8,046 research outputs found
Conversational Exploratory Search via Interactive Storytelling
Conversational interfaces are likely to become more efficient, intuitive and
engaging way for human-computer interaction than today's text or touch-based
interfaces. Current research efforts concerning conversational interfaces focus
primarily on question answering functionality, thereby neglecting support for
search activities beyond targeted information lookup. Users engage in
exploratory search when they are unfamiliar with the domain of their goal,
unsure about the ways to achieve their goals, or unsure about their goals in
the first place. Exploratory search is often supported by approaches from
information visualization. However, such approaches cannot be directly
translated to the setting of conversational search.
In this paper we investigate the affordances of interactive storytelling as a
tool to enable exploratory search within the framework of a conversational
interface. Interactive storytelling provides a way to navigate a document
collection in the pace and order a user prefers. In our vision, interactive
storytelling is to be coupled with a dialogue-based system that provides verbal
explanations and responsive design. We discuss challenges and sketch the
research agenda required to put this vision into life.Comment: Accepted at ICTIR'17 Workshop on Search-Oriented Conversational AI
(SCAI 2017
Conversational Browsing
How can we better understand the mechanisms behind multi-turn information
seeking dialogues? How can we use these insights to design a dialogue system
that does not require explicit query formulation upfront as in question
answering? To answer these questions, we collected observations of human
participants performing a similar task to obtain inspiration for the system
design. Then, we studied the structure of conversations that occurred in these
settings and used the resulting insights to develop a grounded theory, design
and evaluate a first system prototype. Evaluation results show that our
approach is effective and can complement query-based information retrieval
approaches. We contribute new insights about information-seeking behavior by
analyzing and providing automated support for a type of information-seeking
strategy that is effective when the clarity of the information need and
familiarity with the collection content are low
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
Conversational systems typically focus on functional tasks such as scheduling
appointments or creating todo lists. Instead we design and evaluate SlugBot
(SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support
casual open-domain social inter-action. This novel application requires both
broad topic coverage and engaging interactive skills. We developed a new
technical approach to meet this demanding situation by crowd-sourcing novel
content and introducing playful conversational strategies based on storytelling
and games. We collected over 10,000 conversations during August 2018 as part of
the Alexa Prize competition. We also conducted an in-lab follow-up qualitative
evaluation. Over-all users found SB moderately engaging; conversations averaged
3.6 minutes and involved 26 user turns. However, users reacted very differently
to different conversation subtypes. Storytelling and games were evaluated
positively; these were seen as entertaining with predictable interactive
structure. They also led users to impute personality and intelligence to SB. In
contrast, search and general Chit-Chat induced coverage problems; here users
found it hard to infer what topics SB could understand, with these
conversations seen as being too system-driven. Theoretical and design
implications suggest a move away from conversational systems that simply
provide factual information. Future systems should be designed to have their
own opinions with personal stories to share, and SB provides an example of how
we might achieve this.Comment: To appear in 1st International Conference on Conversational User
Interfaces (CUI 2019
Pushed and Non-pushed Speaking Tasks in an EAP Context: What Are the Benefits for Linguistic Processing and Accuracy?
This article reports on a mixed methods study investigating the effectiveness of pushed and non-pushed speaking tasks in a UK university setting with upper-intermediate students. Specifically, the study addressed a) if a pushed speaking task produced more language related episodes (LREs) than a non-pushed speaking task b) the differences in the types of LREs produced by each task and c) whether a pushed speaking task resulted in more accurate usage of past narrative forms. Results showed that the pushed storytelling task produced significantly more LREs than the non-pushed task and it also identified that the most common LRE type for both pushed and non-pushed learners related to some form of output correction. The pushed group achieved greater accuracy gains from pretest and posttest scores but these gain scores were not found to be statistically significant. The study concludes that creating a push during spoken output activities can increase the occurrence of opportunities for linguistic processing, and subsequently interlanguage development, to occur
"Mango Mango, How to Let The Lettuce Dry Without A Spinner?'': Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner
The rapid advancement of the Large Language Model (LLM) has created numerous
potentials for integration with conversational assistants (CAs) assisting
people in their daily tasks, particularly due to their extensive flexibility.
However, users' real-world experiences interacting with these assistants remain
unexplored. In this research, we chose cooking, a complex daily task, as a
scenario to investigate people's successful and unsatisfactory experiences
while receiving assistance from an LLM-based CA, Mango Mango. We discovered
that participants value the system's ability to provide extensive information
beyond the recipe, offer customized instructions based on context, and assist
them in dynamically planning the task. However, they expect the system to be
more adaptive to oral conversation and provide more suggestive responses to
keep users actively involved. Recognizing that users began treating our LLM-CA
as a personal assistant or even a partner rather than just a recipe-reading
tool, we propose several design considerations for future development.Comment: Under submission to CHI202
Artificial intelligence-based conversational agents used for sustainable fashion: systematic literature review.
In the past five years, the textile industry has undergone significant transformations in response to evolving fashion trends and increased consumer garment turnover. To address the environmental impacts of fast fashion, the industry is embracing artificial intelligence (AI) and immersive technologies, particularly leveraging conversational agents as personalised guides for sustainable fashion practices. In this research paper, we conduct a systematic literature review to categorise techniques, platforms, and applications of conversational agents in promoting sustainability within the fashion industry. Additionally, the review aims to scrutinise the solutions offered, identify gaps in the existing literature, and provide insights into the effectiveness and limitations of these conversational agents. Utilising a predefined search strategy on IEEE Xplore, Google Scholar, SCOPUS, and Web of Science, 15 relevant articles were selected through a step-by-step procedure based on the guidelines of the PRISMA framework. The findings reveal a notable global interest in AI-powered conversational agents, with Italy emerging as a significant centre for research in this domain. The studies predominantly focus on consumer perceptions and intentions regarding the adoption of AI technologies, indicating a broader curiosity about how individuals incorporate such innovations into their daily lives. Moreover, a substantial proportion of the studies employs diverse methods, reflecting a comprehensive approach to understanding the functionality and performance of conversational agents in various contexts. While acknowledging the historical precedence of text-based agents, the review highlights a research gap related to embodied agents. The conclusion emphasises the need for continued exploration, particularly in understanding the broader impact of these technologies on creating sustainable and environmentally-friendly business models in the e-retail sector
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