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All-in-one: Multi-task Learning for Rumour Verification

Elena Kochkina, Maria Liakata, Arkaitz Zubiaga

COLING. 2018.

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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.
  title={All-in-one: multi-task learning for rumour verification},
  author={Kochina, Elena and Liakata, Maria and Zubiaga, Arkaitz},
  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
  organization={Association for Computational Linguistics}