In this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
Authors: Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A.
We address discontinuities in our model by utilising the StraightThrough Estimator . During the backward pass we compute the gradients with the Gumbel-softmax reparametrization trick . The same procedure is followed for constructing and sparsifying scene-level graphs in the auxiliary screenplay-based network. A.2. Training on TP Identification Section 3 presents our training regime for the video- and screenplay-based model assuming TP labels for scenes are available .
architecture , then move to discuss how the TP identification network is trained , and finally give technical details about pre-training on screenplays , and the sentiment flow used for graph traversal . A.1.
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Film Trailer Generation via Task Decomposition: Abstract and IntroIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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Film Trailer Generation via Task Decomposition: Related WorkIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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Film Trailer Generation via Task Decomposition: Problem FormulationIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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Film Trailer Generation via Task Decomposition: Results and AnalysisIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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Film Trailer Generation via Task Decomposition: Conclusions and ReferencesIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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Film Trailer Generation via Task Decomposition: Implementation DetailsIn this paper, researchers model movies as graphs to generate trailers, identifying narrative structure and predicting sentiment, surpassing supervised methods.
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