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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
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines—0
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model—0
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation—0
Building a Knowledge-Based Dialogue System with Text Infilling—0
Coordination Generation via Synchronized Text-Infilling—0
Decoding As Dynamic Programming For Recurrent Autoregressive Models—0
Don't Prompt, Search! Mining-based Zero-Shot Learning with Language Models—0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues—0
Flexible-length Text Infilling for Discrete Diffusion Models—0
Generative Prompt Tuning for Relation Classification—0
InFillmore: Frame-Guided Language Generation with Bidirectional Context—0
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer—0
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
On the Role of Bidirectionality in Language Model Pre-Training—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
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