@inproceedings{daniel-etal-2019-towards,
    title = "Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting",
    author = "Daniel, Jeanne E.  and
      Brink, Willie  and
      Eloff, Ryan  and
      Copley, Charles",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/P19-1090/",
    doi = "10.18653/v1/P19-1090",
    pages = "948--953",
    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."
}