81 research outputs found
The Use of Knowledge Preconditions in Language Processing
If an agent does not possess the knowledge needed to perform an action, it
may privately plan to obtain the required information on its own, or it may
involve another agent in the planning process by engaging it in a dialogue. In
this paper, we show how the requirements of knowledge preconditions can be used
to account for information-seeking subdialogues in discourse. We first present
an axiomatization of knowledge preconditions for the SharedPlan model of
collaborative activity (Grosz & Kraus, 1993), and then provide an analysis of
information-seeking subdialogues within a general framework for discourse
processing. In this framework, SharedPlans and relationships among them are
used to model the intentional component of Grosz and Sidner's (1986) theory of
discourse structure.Comment: 7 pages, LaTeX, uses ijcai95.sty, postscript figure
A generic architecture and dialogue model for multimodal interaction
This paper presents a generic architecture and a dialogue model for multimodal interaction. Architecture and model are transparent and have been used for different task domains. In this paper the emphasis is on their use for the navigation task in a virtual environment. The dialogue model is based on the information state approach and the recognition of dialogue acts. We explain how pairs of backward and forward looking tags and the preference rules of the dialogue act determiner together determine the structure of the dialogues that can be handled by the system. The system action selection mechanism and the problem of reference resolution are discussed in detail
Collaborating on Referring Expressions
This paper presents a computational model of how conversational participants
collaborate in order to make a referring action successful. The model is based
on the view of language as goal-directed behavior. We propose that the content
of a referring expression can be accounted for by the planning paradigm. Not
only does this approach allow the processes of building referring expressions
and identifying their referents to be captured by plan construction and plan
inference, it also allows us to account for how participants clarify a
referring expression by using meta-actions that reason about and manipulate the
plan derivation that corresponds to the referring expression. To account for
how clarification goals arise and how inferred clarification plans affect the
agent, we propose that the agents are in a certain state of mind, and that this
state includes an intention to achieve the goal of referring and a plan that
the agents are currently considering. It is this mental state that sanctions
the adoption of goals and the acceptance of inferred plans, and so acts as a
link between understanding and generation.Comment: 32 pages, 2 figures, to appear in Computation Linguistics 21-
Achieving Goals in Collaboration: Analysis of Estonian Institutional Calls
Proceedings of the 16th Nordic Conference
of Computational Linguistics NODALIDA-2007.
Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit.
University of Tartu, Tartu, 2007.
ISBN 978-9985-4-0513-0 (online)
ISBN 978-9985-4-0514-7 (CD-ROM)
pp. 59-66
Developing a corpus of strategic conversation in The Settlers of Catan
International audienceWe describe a dialogue model and an implemented annotation scheme for a pilot corpus of annotated online chats concerning bargaining negotiations in the game The Settlers of Catan. We will use this model and data to analyze how conversations proceed in the absence of strong forms of cooperativity, where agents have diverging motives. Here we concentrate on the description of our annotation scheme for negotiation dialogues, illustrated with our pilot data, and some perspectives for future research on the issue
Grounding or Guesswork? Large Language Models are Presumptive Grounders
Effective conversation requires common ground: a shared understanding between
the participants. Common ground, however, does not emerge spontaneously in
conversation. Speakers and listeners work together to both identify and
construct a shared basis while avoiding misunderstanding. To accomplish
grounding, humans rely on a range of dialogue acts, like clarification (What do
you mean?) and acknowledgment (I understand.). In domains like teaching and
emotional support, carefully constructing grounding prevents misunderstanding.
However, it is unclear whether large language models (LLMs) leverage these
dialogue acts in constructing common ground. To this end, we curate a set of
grounding acts and propose corresponding metrics that quantify attempted
grounding. We study whether LLMs use these grounding acts, simulating them
taking turns from several dialogue datasets, and comparing the results to
humans. We find that current LLMs are presumptive grounders, biased towards
assuming common ground without using grounding acts. To understand the roots of
this behavior, we examine the role of instruction tuning and reinforcement
learning with human feedback (RLHF), finding that RLHF leads to less grounding.
Altogether, our work highlights the need for more research investigating
grounding in human-AI interaction.Comment: 16 pages, 2 figure
Reducing Working Memory Load in Spoken Dialogue Systems
We evaluated two strategies for alleviating working memory load for users of voice interfaces: presenting fewer options per turn and providing confirmations. Forty-eight users booked appointments using nine different dialogue systems, which varied in the number of options presented and the confirmation strategy used. Participants also performed four cognitive tests and rated the usability of each dialogue system on a standardised questionnaire. When systems presented more options per turn and avoided explicit confirmation subdialogues, both older and younger users booked appointments more quickly without compromising task success. Users with lower information processing speed were less likely to remember all relevant aspects of the appointment. Working memory span did not affect appointment recall. Older users were slightly less satisfied with the dialogue systems than younger users. We conclude that the number of options is less important than an accurate assessment of the actual cognitive demands of the task at hand
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The Challenge of Spoken Language Systems: Research Directions for the Nineties
A spoken language system combines speech recognition, natural language processing and human interface technology. It functions by recognizing the person's words, interpreting the sequence of words to obtain a meaning in terms of the application, and providing an appropriate response back to the user. Potential applications of spoken language systems range from simple tasks, such as retrieving information from an existing database (traffic reports, airline schedules), to interactive problem solving tasks involving complex planning and reasoning (travel planning, traffic routing), to support for multilingual interactions. We examine eight key areas in which basic research is needed to produce spoken language systems: (1) robust speech recognition; (2) automatic training and adaptation; (3) spontaneous speech; (4) dialogue models; (5) natural language response generation; (6) speech synthesis and speech generation; (7) multilingual systems; and (8) interactive multimodal systems. In each area, we identify key research challenges, the infrastructure needed to support research, and the expected benefits. We conclude by reviewing the need for multidisciplinary research, for development of shared corpora and related resources, for computational support and far rapid communication among researchers. The successful development of this technology will increase accessibility of computers to a wide range of users, will facilitate multinational communication and trade, and will create new research specialties and jobs in this rapidly expanding area
Using General-Purpose Planning for Action Selection in Human-Robot Interaction
A central problem in designing and implementing interactive
systemsâaction selectionâis also a core research topic in
automated planning. While numerous toolkits are available
for building end-to-end interactive systems, the tight coupling
of representation, reasoning, and technical frameworks found
in these toolkits often makes it difficult to compare or change
the underlying domain models. In contrast, the automated
planning community provides general-purpose representation
languages and multiple planning engines that support these
languages. We describe our recent work on automated planning
for task-based social interaction, using a robot that must
interact with multiple humans in a bartending domain
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