InFillmore: Neural Frame Lexicalization for Narrative Text Infilling.
2021
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 of indistinguishability from the human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo available at this https URL.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
30
References
0
Citations
NaN
KQI