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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 21–30 of 43 papers

TitleStatusHype
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines—0
Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding—0
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
Don't Prompt, Search! Mining-based Zero-Shot Learning with Language Models—0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model—0
Coordination Generation via Synchronized Text-Infilling—0
Building a Knowledge-Based Dialogue System with Text Infilling—0
On the Role of Bidirectionality in Language Model Pre-Training—0
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation—0
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