Uploaded October 2021 | Updated September 2026, 6 days ago
October 2021, Mirella Lapata, professor in the School of Informatics at the University of Edinburgh, whose research focuses on probabilistic learning techniques for natural language understanding and generation, gave a keynote presentation at Amazon's annual machine learning conference.
Mirella's talk centers on Movie analysis as an umbrella term for many tasks aiming to automatically interpret, extract, and summarize the content of a movie. Potential applications include generating shorter versions of scripts to help with the decision-making process in a production company, enhancing movie recommendation engines, and notably generating movie previews.
Mirella introduces the task of turning point identification as a means of analyzing movie content. According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a movie: they define its plot structure, determine its progression and segment it into thematic units.
She argues that turning points and the segmentation they provide can facilitate the analysis of long, complex narratives, such as screenplays. Mirella further formalizes the generation of a shorter version of a movie as the problem of identifying scenes with turning points and present a graph neural network model for this task based on linguistic and audiovisual information.
She ends her discussion on why the representation of screenplays as (sparse) graphs offers interpretability and exposes the morphology of different movie genres.
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October 2021, Mirella Lapata, professor in the School of Informatics at the University of Edinburgh, whose research focuses on probabilistic learning techniques for natural language understanding and generation, gave a keynote presentation at Amazon's annual machine learning conference.
Mirella's talk centers on Movie analysis as an umbrella term for many tasks aiming to automatically interpret, extract, and summarize the content of a movie. Potential applications include generating shorter versions of scripts to help with the decision-making process in a production company, enhancing movie recommendation engines, and notably generating movie previews.
Mirella introduces the task of turning point identification as a means of analyzing movie content. According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a movie: they define its plot structure, determine its progression and segment it into thematic units.
She argues that turning points and the segmentation they provide can facilitate the analysis of long, complex narratives, such as screenplays. Mirella further formalizes the generation of a shorter version of a movie as the problem of identifying scenes with turning points and present a graph neural network model for this task based on linguistic and audiovisual information.
She ends her discussion on why the representation of screenplays as (sparse) graphs offers interpretability and exposes the morphology of different movie genres.
Follow us:
Website: https://www.amazon.science
Twitter: twitter.com/AmazonScience
Facebook: facebook.com/AmazonScience
Instagram: instagram.com/AmazonScience
LinkedIn: linkedin.com/showcase/AmazonScience
Newsletter: https://www.amazon.science/newsletter
#AmazonScience #MachineLearning
