Making neural content meaningful and truthful relying on OpenNLP tools - Boris Galitsky, Jay Taylor
Channel: The ASF
Date: 2021-10-10

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Replicated Making neural content meaningful and truthful relying on OpenNLP tools - Boris Galitsky, Jay Taylor

A presentation from ApacheCon@Home 2021
https://apachecon.com/acah2021

In spite of the great progress neural content generation made over the last few years, the results are noisy and meaningless. In this talk we focus on laying the last mile in robust content generation, taking the neural content as a skeleton and populating it with truthful facts and correct values. To do that, we merge and align syntactic and semantic representation of neural content with that of an existing piece of content from an authoritative source. Overall logic, syntax and content flow are borrowed from the neural content. We form the query from the neural content to mine the web like Wikipedia or another source to obtain the truthful but non-original content to cross-breed with the neural one. As a result, we obtain original high quality content ready for consumption by humans.