294,760 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
Controllable Neural Story Plot Generation via Reinforcement Learning
Language-modeling--based approaches to story plot generation attempt to
construct a plot by sampling from a language model (LM) to predict the next
character, word, or sentence to add to the story. LM techniques lack the
ability to receive guidance from the user to achieve a specific goal, resulting
in stories that don't have a clear sense of progression and lack coherence. We
present a reward-shaping technique that analyzes a story corpus and produces
intermediate rewards that are backpropagated into a pre-trained LM in order to
guide the model towards a given goal. Automated evaluations show our technique
can create a model that generates story plots which consistently achieve a
specified goal. Human-subject studies show that the generated stories have more
plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201
Narrative Generation in Entertainment: Using Artificial Intelligence Planning
From the field of artificial intelligence (AI) there is a growing stream of technology capable of being embedded in software that will reshape the way we interact with our environment in our everyday lives. This āAI softwareā is often used to tackle more mundane tasks that are otherwise dangerous or meticulous for a human to accomplish. One particular area, explored in this paper, is for AI software to assist in supporting the enjoyable aspects of the lives of humans. Entertainment is one of these aspects, and often includes storytelling in some form no matter what the type of media, including television, films, video games, etc. This paper aims to explore the ability of AI software to automate the story-creation and story-telling process. This is part of the field of Automatic Narrative Generator (ANG), which aims to produce intuitive interfaces to support people (without any previous programming experience) to use tools to generate stories, based on their ideas of the kind of characters, intentions, events and spaces they want to be in the story. The paper includes details of such AI software created by the author that can be downloaded and used by the reader for this purpose. Applications of this kind of technology include the automatic generation of story lines for āsoap operasā
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Plan-And-Write: Towards Better Automatic Storytelling
Automatic storytelling is challenging since it requires generating long,
coherent natural language to describes a sensible sequence of events. Despite
considerable efforts on automatic story generation in the past, prior work
either is restricted in plot planning, or can only generate stories in a narrow
domain. In this paper, we explore open-domain story generation that writes
stories given a title (topic) as input. We propose a plan-and-write
hierarchical generation framework that first plans a storyline, and then
generates a story based on the storyline. We compare two planning strategies.
The dynamic schema interweaves story planning and its surface realization in
text, while the static schema plans out the entire storyline before generating
stories. Experiments show that with explicit storyline planning, the generated
stories are more diverse, coherent, and on topic than those generated without
creating a full plan, according to both automatic and human evaluations.Comment: Accepted by AAAI 201
The automatic generation of narratives
We present the Narrator, a Natural Language Generation component used in a digital storytelling system. The system takes as input a formal representation of a story plot, in the form of a causal network relating the actions of the characters to their motives and their consequences. Based on this input, the Narrator generates a narrative in Dutch, by carrying out tasks such as constructing a Document Plan, performing aggregation and ellipsis and the generation of appropriate referring expressions. We describe how these tasks are performed and illustrate the process with examples, showing how this results in the generation
of coherent and well-formed narrative texts
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