456 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
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Given the increasing popularity of customer service dialogue on Twitter,
analysis of conversation data is essential to understand trends in customer and
agent behavior for the purpose of automating customer service interactions. In
this work, we develop a novel taxonomy of fine-grained "dialogue acts"
frequently observed in customer service, showcasing acts that are more suited
to the domain than the more generic existing taxonomies. Using a sequential
SVM-HMM model, we model conversation flow, predicting the dialogue act of a
given turn in real-time. We characterize differences between customer and agent
behavior in Twitter customer service conversations, and investigate the effect
of testing our system on different customer service industries. Finally, we use
a data-driven approach to predict important conversation outcomes: customer
satisfaction, customer frustration, and overall problem resolution. We show
that the type and location of certain dialogue acts in a conversation have a
significant effect on the probability of desirable and undesirable outcomes,
and present actionable rules based on our findings. The patterns and rules we
derive can be used as guidelines for outcome-driven automated customer service
platforms.Comment: 13 pages, 6 figures, IUI 201
INFACT: An Online Human Evaluation Framework for Conversational Recommendation
Conversational recommender systems (CRS) are interactive agents that support
their users in recommendation-related goals through multi-turn conversations.
Generally, a CRS can be evaluated in various dimensions. Today's CRS mainly
rely on offline(computational) measures to assess the performance of their
algorithms in comparison to different baselines. However, offline measures can
have limitations, for example, when the metrics for comparing a newly generated
response with a ground truth do not correlate with human perceptions, because
various alternative generated responses might be suitable too in a given dialog
situation. Current research on machine learning-based CRS models therefore
acknowledges the importance of humans in the evaluation process, knowing that
pure offline measures may not be sufficient in evaluating a highly interactive
system like a CRS.Comment: 6 pages, 2 figures
Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users' interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods
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