Key points are not available for this paper at this time.
There are obvious reasons for trying to automate the production of multilingual documentation, especially for routine subject-matter in restricted domains (e.g. technical instructions). Two approaches have been adopted: Machine Translation (MT) of a source text, and Multilingual Natural Language Generation (M-NLG) from a knowledge base. For MT, information extraction is a major difficulty, since the meaning must be derived by analysis of the source text; M-NLG avoids this difficulty but seems at first sight to require an expensive phase of knowledge engineering in order to encode the meaning. We introduce here a new technique which employs M-NLG during the phase of knowledge editing. A 'feedback text', generated from a possibly incomplete knowledge base, describes in natural language the knowledge encoded so far, and the options for extending it. This method allows anyone speaking one of the supported languages to produce texts in all of them, requiring from the author only expertise in the subject-matter, not expertise in knowledge engineering.
Power et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: