38,843 research outputs found
Exploiting visual salience for the generation of referring expressions
In this paper we present a novel approach to generating
referring expressions (GRE) that is tailored to a model of the visual context the user is attending to. The approach
integrates a new computational model of visual salience in simulated 3-D environments with Dale and Reiter’s (1995) Incremental Algorithm. The advantage of our GRE framework are: (1) the context set used by the GRE algorithm is dynamically computed by the visual saliency algorithm as a user navigates through a simulation; (2) the integration of visual salience into the generation process means that in some instances underspecified but sufficiently detailed descriptions of the target object are generated that are shorter than those generated by GRE algorithms which focus purely on adjectival and type attributes; (3) the integration of visual saliency into the generation process means that our GRE algorithm will in some instances succeed in generating a description of the target object in situations where GRE algorithms which focus purely on adjectival and type attributes fail
A Flexible pragmatics-driven language generator for animated agents
This paper describes the NECA MNLG; a fully implemented Multimodal Natural Language Generation module. The MNLG is deployed as part of the NECA system which generates dialogues between animated agents. The generation module supports the seamless integration of full grammar rules, templates and canned text. The generator takes input which allows for the specification of syntactic, semantic and pragmatic constraints on the output
Atlas.txt : Exploring Lingusitic Grounding Techniques for Communicating Spatial Information to Blind Users
Peer reviewedPostprin
Textual Economy through Close Coupling of Syntax and Semantics
We focus on the production of efficient descriptions of objects, actions and
events. We define a type of efficiency, textual economy, that exploits the
hearer's recognition of inferential links to material elsewhere within a
sentence. Textual economy leads to efficient descriptions because the material
that supports such inferences has been included to satisfy independent
communicative goals, and is therefore overloaded in Pollack's sense. We argue
that achieving textual economy imposes strong requirements on the
representation and reasoning used in generating sentences. The representation
must support the generator's simultaneous consideration of syntax and
semantics. Reasoning must enable the generator to assess quickly and reliably
at any stage how the hearer will interpret the current sentence, with its
(incomplete) syntax and semantics. We show that these representational and
reasoning requirements are met in the SPUD system for sentence planning and
realization.Comment: 10 pages, uses QobiTree.te
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