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Explainable and Robust Automatic Fact Checking

Project description

Automatically predicting what’s a fact and what’s not

The expanded reach of the internet and media as well as impactful recent events have made it necessary to quickly and easily verify facts online. Unfortunately, complicating factors like the massive amounts of data mean that even machine-learning-based fact-checking has difficulty either efficiently achieving this task or further explaining its process for fact verification. Enter the European Research Council-funded ExplainYourself project, which will provide explainable fact checking. Since automatic fact-checking methods often use opaque deep neural networks, the project will provide explainable fact checking. Existing approaches are unable to produce diverse explanations, geared towards users with different information needs.


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.


Net EU contribution
€ 1 498 616,00
Norregade 10
1165 Kobenhavn

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Danmark Hovedstaden Byen København
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00