Making neural content meaningful and truthful - Livestream

A text obtained by a deep learning (DL) content generation system (raw text) usually has major issues in terms of randomness and incorrectness. We build a content improvement system specifically oriented towards repairing these errors by finding correct and consistent sentences from various sources and substituting problematic entities, phrases, and sentences in raw content with the correct ones. We use text mining to identify correct corresponding sentences and the syntactic and semantic generalization procedure adopted to the content improvement task. We observed that raw content produced by a DL system like GPT-3 can be substantially improved for factual correctness and meaningfulness

Speaker: Boris Galitsky, linguist and machine language technologies

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