# Adversarial Demonstration Learning for Low-resource NER Using Dual Similarity

arXiv: https://arxiv.org/abs/2507.15864 · HTML: https://mlsanity.com/paper/2507.15864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 19 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://mlsanity.com/paper/2203.08773/) (2022)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [Label Semantics for Few Shot Named Entity Recognition](https://mlsanity.com/paper/2203.08985/) (2022)
- [Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation](https://mlsanity.com/paper/2108.11703/) (2021)

## Abstract (arXiv)

We study the problem of named entity recognition (NER) based on demonstration learning in low-resource scenarios. We identify two issues in demonstration construction and model training. Firstly, existing methods for selecting demonstration examples primarily rely on semantic similarity; We show that feature similarity can provide significant performance improvement. Secondly, we show that the NER tagger's ability to reference demonstration examples is generally inadequate. We propose a demonstration and training approach that effectively addresses these issues. For the first issue, we propose to select examples by dual similarity, which comprises both semantic similarity and feature similarity. For the second issue, we propose to train an NER model with adversarial demonstration such that the model is forced to refer to the demonstrations when performing the tagging task. We conduct comprehensive experiments in low-resource NER tasks, and the results demonstrate that our method outperforms a range of methods.