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AVeriTeC

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Automated Verification of Textual Claims

Verification of textual claims is the task of assessing the truthfulness of a statement in natural language. It is commonly conducted manually by journalists on claims made by public figures such as politicians, with the aim of re... ver más

31/12/2025

THE CHANCELLOR MAS...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-02-03

ERC-2019-COG: ERC Consolidator Grant

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2 Participantes

THE CHANCELLOR MASTERS AND S...

1.98M€ | Lider

CASA AMETLLER

666.22K€ | Participante

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Duración del proyecto: 70 meses Fecha Inicio: 2020-02-03
Fecha Fin: 2025-12-31

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Verification of textual claims is the task of assessing the truthfulness of a statement in natural language. It is commonly conducted manually by journalists on claims made by public figures such as politicians, with the aim of reducing misinformation. However, the proliferation of social media has created the need to apply verification to a larger volume of claims coming from a greater variety of sources, thus calling for automation. Research in automated verification of textual claims is at an early stage. The methods developed either assess the truthfulness of the claim without considering evidence, or handle very simple claims such as UK has 3.2 million EU immigrants that requires the retrieval of a single factoid from a knowledge base. While useful, claims are often more complex, and taking evidence into account is necessary for the verdicts to be credible. AVeriTeC will transform automated verification by enabling the verification of more complex claims than previously attempted, such as the United Kingdom has ten times Italy’s number of immigrants, which require multiple pieces of evidence. We will achieve this by developing methods able to generate multiple questions per claim, retrieve answers from both knowledge bases and textual sources, and combine them into verdicts. As these tasks are interdependent, we will develop novel machine learning approaches able to handle them jointly so that the verdicts are accompanied by suitable justifications in the form of questions and answers. The latter will be formulated in natural language, thus the process followed by the models developed will be explainable to the users, while the evidence itself can be useful even if the overall verdict is incorrect. Beyond developing novel methods and creating publicly available evaluation resources, AVeriTeC will establish verification of textual claims as a real-world challenge to stimulate progress in natural language processing, machine learning and related fields.

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