  We propose a structured extension to bidirectional-context conditional
language generation, or "infilling," inspired by Frame Semantic theory
(Fillmore, 1976). Guidance is provided through two approaches: (1) model
fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel
extension to disjunctive lexically constrained decoding that leverages frame
semantic lexical units. Automatic and human evaluations confirm that
frame-guided generation allows for explicit manipulation of intended infill
semantics, with minimal loss in distinguishability from human-generated text.
Our methods flexibly apply to a variety of use scenarios, and we provide a
codebase and interactive demo available from
https://nlp.jhu.edu/demos/infillmore.
