Project description DEENESFRITPL Just the facts, please! Journalism is about disseminating facts. As such, a reporter’s job is to check the facts being presented to the public. In recent years, the verification of social media content has become increasingly important to journalists and news organisations. The proliferation of social media means a larger volume of claims to verify; therefore, automated verification methods could help journalists assess the truthfulness of claims. The EU-funded AVeriTeC project will use machine learning approaches to develop an automated verification system that can process complex claims that require multiple pieces of evidence to cross-check. AVeriTeC’s ultimate goal is to establish the verification of textual claims as a real-world challenge to stimulate progress in natural language processing, machine learning and related fields. Show the project objective Hide the project objective Objective 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. Fields of science social sciencesmedia and communicationsjournalismnatural sciencescomputer and information sciencesdata sciencenatural language processingsocial sciencessociologyindustrial relationsautomationnatural sciencescomputer and information sciencesartificial intelligencemachine learningsocial sciencessociologydemographyhuman migrations Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2019-COG - ERC Consolidator Grant Call for proposal ERC-2019-COG See other projects for this call Funding Scheme ERC-COG - Consolidator Grant Coordinator THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE Net EU contribution € 1 982 824,00 Address Trinity lane the old schools CB2 1TN Cambridge United Kingdom See on map Region East of England East Anglia Cambridgeshire CC Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE United Kingdom Net EU contribution € 1 982 824,00 Address Trinity lane the old schools CB2 1TN Cambridge See on map Region East of England East Anglia Cambridgeshire CC Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
