Uncovering the creative process: from inception to reception of translated content using machine translation
INCREC aims to analyze the creative processes of professional translators using neural machine translation to enhance translation quality and user experience in literary and audiovisual contexts.
Projectdetails
Introduction
The new machine translation paradigm, neural machine translation, has drastically changed society’s perception of technology and the act of translation. It has also motivated a series of claims by research labs and the media implying that machines will soon decode the transfer of one language to another, thus driving professional translators out of their jobs.
Impact on Creative Content Producers
Creative content producers, such as streaming platforms or publishing houses, are now exploring, testing, or using neural machine translation. Yet little is known of the constraints this technology poses to translators’ creative process and how such constraints impact the users. The need to understand this has become essential to sustain translation richness, translators’ job satisfaction, authors’ and directors’ reputations, and the user experience.
Project Overview
INCREC aims to uncover the creative stages of professional translators to understand how technology can be best applied to the translation of literary and audiovisual texts, and to analyze the impact of these processes on readers and viewers.
Work Packages
The research project is articulated in four work packages that cover two broad areas: inception and reception of literary and audiovisual translation. To better understand this complex process, INCREC triangulates data from:
- Eye-tracking
- Retrospective think-aloud interviews
- Translated material
- Questionnaires from professional translators and users
Theoretical Framework
Thus, INCREC develops a new theoretical framework that encompasses:
a) Creative stages in translation
b) Classification and mapping of translation problems which require a higher level of creativity and cognition, as well as a classification of the solutions to these problems
c) Understanding how machine translation is most effectively used during this creative process
d) Understanding user attention to creativity in translated literary texts and films
This is achieved using a new combination of methods from different disciplines.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.993.643 |
Totale projectbegroting | € 1.993.643 |
Tijdlijn
Startdatum | 1-9-2023 |
Einddatum | 31-8-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- RIJKSUNIVERSITEIT GRONINGENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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The neural mechanism of scale-invariant creative searchCreativeBrain aims to unify behavioral, computational, and neurobiological insights into a mechanistic theory of creative search in the brain using scale-invariant sensing and Pareto optimality. | ERC Consolid... | € 1.998.000 | 2023 | Details |
Tensors and Neural Networks for Computational Creativity
This project aims to develop unsupervised language models using tensor constructs and advanced neural networks to enhance creativity in natural language generation.
A Systematic Exploration of Interreal Translations in the Media Multiverse
InterReal aims to develop a theoretical framework and innovative methods to study interreal translations within a Media Multiverse, enhancing understanding of diverse media-generated realities.
A prototype system for obtaining and managing training data for multilingual learning
The project aims to empower less-resourced language communities to create parallel corpora for machine translation, enhancing language preservation and cultural heritage through an open-source prototype.
DEep COgnition Learning for LAnguage GEneration
This project aims to enhance NLP models by integrating machine learning, cognitive science, and structured memory to improve out-of-domain generalization and contextual understanding in language generation tasks.
The neural mechanism of scale-invariant creative search
CreativeBrain aims to unify behavioral, computational, and neurobiological insights into a mechanistic theory of creative search in the brain using scale-invariant sensing and Pareto optimality.
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