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SUMMARY:ML-Opt @ UWash: Sean Welleck
DESCRIPTION:Speaker: Sean Welleck \nTitle: Constrained text generation through discrete and continuous inference \nAbstract: Neural text generation has shifted to generating text with large-scale\, general-purpose models coupled with generic inference algorithms. An important open question is how to efficiently offer control over generated text. We describe two algorithms for enabling control through inference-time constraints: (i) A*-Neurologic\, a discrete search algorithm for incorporating logical constraints through estimates of the future\, and (ii) COLD decoding\, which treats generation as continuous gradient-based sampling from an energy function that captures task-relevant constraints. Our algorithms can be applied directly to off-the-shelf models without the need for task-specific finetuning\, and result in strong performance on a variety of generation tasks.  
URL:https://ifds.info/event/ml-opt-uwash-sean-welleck/
CATEGORIES:MLOpt@UWash
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