All-in-one: Multi-task Learning for Rumour Verification
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Automatic resolution of rumours is a challenging task that can be broken down into smaller
components that make up a pipeline, including rumour detection, rumour tracking and stance
classification, leading to the final outcome of determining the veracity of a rumour. In previous
work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach
that allows joint training of the main and auxiliary tasks, improving the performance of rumour
verification. We examine the connection between the dataset properties and the outcomes of the
multi-task learning models used.