Evaluation of a Runyankore grammar engine for healthcare messages

Joan Byamugisha, C. Maria Keet, Brian DeRenzi


Abstract
Natural Language Generation (NLG) can be used to generate personalized health information, which is especially useful when provided in one’s own language. However, the NLG technique widely used in different domains and languages—templates—was shown to be inapplicable to Bantu languages, due to their characteristic agglutinative structure. We present here our use of the grammar engine NLG technique to generate text in Runyankore, a Bantu language indigenous to Uganda. Our grammar engine adds to previous work in this field with new rules for cardinality constraints, prepositions in roles, the passive, and phonological conditioning. We evaluated the generated text with linguists and non-linguists, who regarded most text as grammatically correct and understandable; and over 60% of them regarded all the text generated by our system to have been authored by a human being.
Anthology ID:
W17-3514
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
105–113
Language:
URL:
https://www.aclweb.org/anthology/W17-3514
DOI:
10.18653/v1/W17-3514
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-3514.pdf