Project description
Unlocking the future of language understanding and processing
In recent years, transformer-based deep-learning models like BERT and GPT-3 have dazzled us with their natural language processing (NLP) prowess, showcasing remarkable transfer and few-shot learning abilities. However, these models often stumble when pushed beyond their comfort zones, lacking out-of-domain adaptability, contextual understanding, calibration and traceable memory. Funded by the European Research Council, the DECOLLAGE project will address these challenges. Specifically, it will develop utility-guided controlled generation with uncertainty estimates. It will also integrate contextual information efficiently, and craft sparse memory models. DECOLLAGE fosters conscious processing through attention descriptive representations and enables seamless communication among modules and agents. With applications spanning machine translation, open dialogue and story generation, DECOLLAGE will ensure NLP’s evolution stays ahead of the curve.
Objective
In recent years, transformer-based deep learning models such as BERT or GPT-3 have led to impressive results in many natural language processing (NLP) tasks, exhibiting transfer and few-shot learning capabilities.
However, despite faring well in benchmarks, current deep learning models for NLP often fail badly in the wild: they are bad at out-of-domain generalization, they do not exploit contextual information, they are poorly calibrated, and their memory is not traceable. These limitations stem from their monolithic architectures, which are good for perception, but unsuitable for tasks requiring higher-level cognition.
In this project, I attack these fundamental problems by bringing together tools and ideas from machine learning, sparse modeling, information theory, and cognitive science, in an interdisciplinary approach. First, I will use uncertainty and quality estimates for utility-guided controlled generation, combining this control mechanism with the efficient encoding of contextual information and integration of multiple modalities. Second, I will develop sparse and structured memory models, together with attention descriptive representations towards conscious processing. Third, I will build mathematical models for sparse communication (reconciling discrete and continuous domains), supporting end-to-end differentiability and enabling a shared workspace where multiple modules and agents can communicate.
I will apply the innovations above to highly challenging language generation tasks, including machine translation, open dialogue, and story generation. To reinforce interdisciplinarity and maximize technological impact, collaborations are planned with cognitive scientists and with a scale-up company in the crowd-sourcing translation industry.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3810-193 GLORIA E VERA CRUZ
Portugal
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.