Papers with 1-shot
Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)
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| Challenge: | generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks. |
| Approach: | They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks . |
| Outcome: | The proposed model performs on few-shot learning, slot labeling, and intent classification benchmarks. |
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent (2025.coling-main)
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Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, Yi Fung, Preslav Nakov, Julia Hirschberg, Heng Ji
| Challenge: | Existing research on propaganda detection does not capture the motives behind the content or its broader impact. |
| Approach: | They propose a framework that dissects propaganda into techniques, arousal appeals, and underlying intent. |
| Outcome: | The proposed framework improves performance in a wide range of scenarios and can be used to identify and categorize propaganda techniques. |
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation (2023.acl-long)
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| Challenge: | Existing methods for fewshot text classification depend on inter-class variance . Existing approaches suffer from MLADA, which performs poorly on tasks with high inter- class variance whereas it fails to distinguish samples from tasks with low inter-group variance. |
| Approach: | They propose a task-adaptive reference transformation network to transform class prototypes to per-class fixed reference points in task-adapted metric spaces. |
| Outcome: | The proposed method surpasses state-of-the-art methods in 1-shot and 5-shot classifications on the 20 Newsgroups dataset. |
PPTSER: A Plug-and-Play Tag-guided Method for Few-shot Semantic Entity Recognition on Visually-rich Documents (2024.findings-acl)
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| Challenge: | Existing methods for visually-rich document information extraction are limited . Xu et al., 2020: visually rich document information is a vital aspect of document understanding . |
| Approach: | They propose a plug-and-play Tag-guided method for few-shot Semantic Entity Recognition (PPTSER) on visually-rich documents. |
| Outcome: | The proposed method outperforms fine-tuning and few-shot methods on visual-rich documents. |
HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing adaptation methods overlook structural knowledge between text and image modalities or create overly complex graphs containing redundant information for alignment. |
| Approach: | They propose a method to adapt visual models to downstream tasks using text and image modalities. |
| Outcome: | The proposed method improves classification accuracy by 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets. |