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        <title>Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting
%A Daniel, Jeanne E.
%A Brink, Willie
%A Eloff, Ryan
%A Copley, Charles
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F daniel-etal-2019-towards
%X 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.
%R 10.18653/v1/P19-1090
%U https://aclanthology.org/P19-1090
%U https://doi.org/10.18653/v1/P19-1090
%P 948-953

Markdown (Informal)

[Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting](https://aclanthology.org/P19-1090) (Daniel et al., ACL 2019)

• Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting (Daniel et al., ACL 2019)

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.