Learning Distributions over Logical Forms for Referring Expression GenerationLearning Distributions over Logical Forms for Referring Expression GenerationFitzGerald, Nicholas and Artzi, Yoav and Zettlemoyer, Luke S.2013
Paper summarymihail911 * 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
* 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