2024-03-28T13:47:09Z
https://pubs.cs.uct.ac.za/cgi/oai2
oai:pubs.cs.uct.ac.za:1236
2019-10-10T15:31:45Z
7375626A656374733D3130303130313437:3130303130313738
74797065733D636F6E667061706572
https://pubs.cs.uct.ac.za/id/eprint/1236/
Evaluation of a Runyankore grammar engine for healthcare messages
Byamugisha, Ms Joan
Keet, Dr. C. Maria
DeRenzi, Dr. Brian
Artificial intelligence
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.
ALC
2017
Conference paper
application/pdf
en
https://pubs.cs.uct.ac.za/id/eprint/1236/1/EvalRunyanINLG17.pdf
Byamugisha, Ms Joan and Keet, Dr. C. Maria and DeRenzi, Dr. Brian (2017) Evaluation of a Runyankore grammar engine for healthcare messages, Proceedings of 10th International Natural Language Generation conference (INLG'17), 4-7 Sept 2017, Santiago de Compostela, Spain, 105-113, ALC.