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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C18-1288.pdf