Learning Distributions over Logical Forms for Referring Expression Generation Learning Distributions over Logical Forms for Referring Expression Generation
Paper summary * Efficiently find fraction of referring expressions for scenes that are used; estimate associated likelihoods * Learn probability distribution over set of logical expressions that select a target set of objects in a world state * Model as globally normalized log-linear model using features of logical form *z* * Distinction for plural entities in generated logical forms * globally optimized log-linear model, conditioned on state S and set of target objects G * Three kinds of features: logical expression structure features, situated features, and a complexity feature * learning two models--one for a global logical form for world-state model; and one learning a series of classifiers for each * learn codebooks and associated sparse codes
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Learning Distributions over Logical Forms for Referring Expression Generation
FitzGerald, Nicholas and Artzi, Yoav and Zettlemoyer, Luke S.
Empirical Methods on Natural Language Processing (EMNLP) - 2013 via Local Bibsonomy
Keywords: dblp


Summary by Mihail Eric 7 months ago
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