Abstract
We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.- Anthology ID:
- W18-2302
- Volume:
- Proceedings of the BioNLP 2018 workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–17
- Language:
- URL:
- https://aclanthology.org/W18-2302
- DOI:
- 10.18653/v1/W18-2302
- Cite (ACL):
- Serena Jeblee, Mireille Gomes, and Graeme Hirst. 2018. Multi-task learning for interpretable cause of death classification using key phrase prediction. In Proceedings of the BioNLP 2018 workshop, pages 12–17, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Multi-task learning for interpretable cause of death classification using key phrase prediction (Jeblee et al., BioNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-2302.pdf