Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting

Jeanne E. Daniel, Willie Brink, Ryan Eloff, Charles Copley


Abstract
We discuss ongoing work into automating a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. Our anonymized dataset consists of short informal questions, often in low-resource languages, with unreliable language labels, spelling errors and code-mixing, as well as template answers with some inconsistencies. We explore cross-lingual word embeddings, and train parametric and non-parametric models on 90K samples for answer selection from a set of 126 templates. Preliminary results indicate that LSTMs trained end-to-end perform best, with a test accuracy of 62.13% and a recall@5 of 89.56%, and demonstrate that we can accelerate response time by several orders of magnitude.
Anthology ID:
P19-1090
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
948–953
Language:
URL:
https://aclanthology.org/P19-1090
DOI:
10.18653/v1/P19-1090
Bibkey:
Cite (ACL):
Jeanne E. Daniel, Willie Brink, Ryan Eloff, and Charles Copley. 2019. Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 948–953, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting (Daniel et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1090.pdf
Video:
 https://aclanthology.org/P19-1090.mp4