It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction
This addresses the problem of efficient fact-checking for political analysts and organizations, though it is incremental as it builds on existing multi-task learning methods.
The paper tackles predicting which statements in political debates should be prioritized for fact-checking by proposing a multi-task deep-learning approach that learns from multiple sources simultaneously, achieving state-of-the-art results on a standard dataset.
We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.