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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P19-1090.pdf