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

InFillmore: Frame-Guided Language Generation with Bidirectional Context