# Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding

arXiv: https://arxiv.org/abs/2009.09568 · HTML: https://mlsanity.com/paper/2009.09568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2312.13495/) (2023)
- [Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning](https://mlsanity.com/paper/2109.05234/) (2021)

## Abstract (arXiv)

Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems. In this paper, we propose a vector projection network for few-shot slot tagging, which exploits projections of contextual word embeddings on each target label vector as word-label similarities. Essentially, this approach is equivalent to a normalized linear model with an adaptive bias. The contrastive experiment demonstrates that our proposed vector projection based similarity metric can significantly surpass other variants. Specifically, in the five-shot setting on benchmarks SNIPS and NER, our method outperforms the strongest few-shot learning baseline by 6.30 and 13.79 points on F1 score, respectively. Our code will be released at https://github.com/sz128/fewshotslottaggingandNER.