Multi-task learning for interpretable cause of death classification using key phrase prediction

Serena Jeblee, Mireille Gomes, Graeme Hirst


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
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–17
Language:
URL:
https://aclanthology.org/W18-2302
DOI:
10.18653/v1/W18-2302
Bibkey:
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., 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2302.pdf