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

Elena Kochkina, Maria Liakata, Arkaitz Zubiaga


Abstract
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.
Anthology ID:
C18-1288
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3402–3413
Language:
URL:
https://aclanthology.org/C18-1288
DOI:
Bibkey:
Cite (ACL):
Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018. All-in-one: Multi-task Learning for Rumour Verification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3402–3413, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
All-in-one: Multi-task Learning for Rumour Verification (Kochkina et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1288.pdf