# Data Augmentation for Cross-Domain Named Entity Recognition

arXiv: https://arxiv.org/abs/2109.01758 · HTML: https://mlsanity.com/paper/2109.01758/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 4 Sep 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://mlsanity.com/paper/2004.11999/) (2020)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [Generalized Data Augmentation for Low-Resource Translation](https://mlsanity.com/paper/1906.03785/) (2019)
- [SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup](https://mlsanity.com/paper/2010.02322/) (2020)
- [Soft Contextual Data Augmentation for Neural Machine Translation](https://mlsanity.com/paper/1905.10523/) (2019)
- [Data Augmentation via Dependency Tree Morphing for Low-Resource Languages](https://mlsanity.com/paper/1903.09460/) (2019)
- [Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study](https://mlsanity.com/paper/2001.03844/) (2020)
- [Zero-Resource Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2002.05923/) (2020)

## Built on by (most cited first, 14 of 14)

- [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://mlsanity.com/paper/2305.17104/) (2023)
- [A Survey of Data Synthesis Approaches](https://mlsanity.com/paper/2407.03672/) (2024)
- [FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition](https://mlsanity.com/paper/2208.11464/) (2022)
- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)
- [AUC Maximization for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2212.04800/) (2022)
- [Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction](https://mlsanity.com/paper/2306.14122/) (2023)
- [VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models](https://mlsanity.com/paper/2402.18374/) (2024)
- [Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?](https://mlsanity.com/paper/2407.02062/) (2024)
- [Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition](https://mlsanity.com/paper/2210.07916/) (2022)
- [MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts](https://mlsanity.com/paper/2211.13896/) (2022)
- [Data Augmentation for Robust Character Detection in Fantasy Novels](https://mlsanity.com/paper/2302.04555/) (2023)
- [Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2407.17344/) (2024)
- [Enhancing Few-shot NER with Prompt Ordering based Data Augmentation](https://mlsanity.com/paper/2305.11791/) (2023)
- [CodeNER: Code Prompting for Named Entity Recognition](https://mlsanity.com/paper/2507.20423/) (2025)

## Abstract (arXiv)

Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In contrast, we study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.