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ExplainYourself

Financiado

Explainable and Robust Automatic Fact Checking

ExplainYourself proposes to study explainable automatic fact checking, the task of automatically predicting the veracity of textual claims using machine learning (ML) methods, while also producing explanations about how the model... ver más

31/08/2028

UCPH

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-12-17

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2022-STG: ERC STARTING GRANTS

I+D

Cerrada hace 2 años

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1 Participantes

UCPH

1.50M€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 68 meses Fecha Inicio: 2022-12-17
Fecha Fin: 2028-08-31

Líder del proyecto

KOBENHAVNS UNIVERSITET

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto ExplainYourself proposes to study explainable automatic fact checking, the task of automatically predicting the veracity of textual claims using machine learning (ML) methods, while also producing explanations about how the model arrived at the prediction. Automatic fact checking methods often use opaque deep neural network models, whose inner workings cannot easily be explained. Especially for complex tasks such as automatic fact checking, this hinders greater adoption, as it is unclear to users when the models' predictions can be trusted. Existing explainable ML methods partly overcome this by reducing the task of explanation generation to highlighting the right rationale. While a good first step, this does not fully explain how a ML model arrived at a prediction. For knowledge intensive natural language understanding (NLU) tasks such as fact checking, a ML model needs to learn complex relationships between the claim, multiple evidence documents, and common sense knowledge in addition to retrieving the right evidence. There is currently no explainability method that aims to illuminate this highly complex process. In addition, existing approaches are unable to produce diverse explanations, geared towards users with different information needs. ExplainYourself radically departs from existing work in proposing methods for explainable fact checking that more accurately reflect how fact checking models make decisions, and are useful to diverse groups of end users. It is expected that these innovations will apply to explanation generation for other knowledge-intensive NLU tasks, such as question answering or entity linking. To achieve this, ExplainYourself builds on my pioneering work on explainable fact checking as well as my interdisciplinary expertise.

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