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Text Infilling

Text Infilling is the task of predicting missing spans of text which are consistent with the preceding and subsequent text. Text Infilling is a generalization of the cloze task—cloze historically refers to infilling individual words.

Source: Enabling Language Models to Fill in the Blanks

Papers

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Showing 26–43 of 43 papers

TitleStatusHype
InFillmore: Frame-Guided Language Generation with Bidirectional Context—0
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer—0
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
Predicting scalar diversity with context-driven uncertainty over alternatives—0
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models—0
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding—0
TIGS: An Inference Algorithm for Text Infilling with Gradient SearchCode0
Text InfillingCode0
Towards Probabilistically-Sound Beam Search with Masked Language ModelsCode0
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text ExchangeCode0
Conformal prediction for text infilling and part-of-speech predictionCode0
Empowering Character-level Text Infilling by Eliminating Sub-TokensCode0
TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal ModelCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNetCode0
Nutribullets Hybrid: Multi-document Health SummarizationCode0
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