Descrizione del progetto
Previsione automatica della veridicità dei fatti
L’espansione della portata di Internet e dei media, nonché eventi recenti di grande impatto, hanno reso necessaria una verifica rapida e semplice dei fatti online. Purtroppo, fattori di complessità quali l’enorme mole di dati fanno sì che anche il fact-checking basato sull’apprendimento automatico presenti alcune difficoltà a svolgere il compito in modo efficace o a spiegare approfonditamente il processo seguito per la verifica dei fatti. Il progetto ExplainYourself, finanziato dal Consiglio europeo della ricerca, fornirà un processo di verifica spiegabile, a differenza dei metodi automatici di fact-checking che spesso utilizzano reti neurali profonde poco trasparenti. Gli approcci esistenti non sono in grado di produrre spiegazioni diversificate, rivolte a utenti con esigenze informative differenti.
Obiettivo
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.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
ERC - Support for frontier research (ERC)Istituzione ospitante
1165 Kobenhavn
Danimarca