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How to Build a Natural Language Generator Using AI?

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Building Natural Language Generators (NLG systems) is not easy. First, Natural Language Generator building heavily depends on the purpose, just like how your written communication varies with the purpose. Let us see how your approach change with the type of content you write. For instance, you will need to apply a lot of creativity while writing a blog post; curation requires that your content and world knowledge are based on the input document or a set of documents/pages; while reports are mostly templates & use Natural Language Generation AI to realize the sentences and NLG for chatbots requires NLU/intent engines in addition to templates applied to outputs.

For certain content, Natural Language Generator is free to produce very similar results(chatbots, reports, etc) but for some content types, it cannot (plagiarism issues in blogs). So the randomness of the content/lexical selection varies with content types. It’s better to work backward and decide on the modules required for the NLG than going with a fixed architecture from the top. Most research material you find online is for a structured content generation using Artificial Intelligence like generating reports. So, you will have to work a bit harder than that for other content types.

Take a look at the NLG content generator we at Adzis, Inc have built specifically for ecommerce businesses here:


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