# Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting

arXiv: https://arxiv.org/abs/2204.11406 · HTML: https://mlsanity.com/paper/2204.11406/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 61 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 30 of 37)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Augmenting Data with Mixup for Sentence Classification: An Empirical Study](https://mlsanity.com/paper/1905.08941/) (2019)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://mlsanity.com/paper/1809.08370/) (2018)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [AEDA: An Easier Data Augmentation Technique for Text Classification](https://mlsanity.com/paper/2108.13230/) (2021)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup](https://mlsanity.com/paper/2010.02322/) (2020)
- [Glyce: Glyph-vectors for Chinese Character Representations](https://mlsanity.com/paper/1901.10125/) (2019)
- [Parallel Data Augmentation for Formality Style Transfer](https://mlsanity.com/paper/2005.07522/) (2020)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [Local Additivity Based Data Augmentation for Semi-supervised NER](https://mlsanity.com/paper/2010.01677/) (2020)
- [Simplify the Usage of Lexicon in Chinese NER](https://mlsanity.com/paper/1908.05969/) (2019)
- [Named Entity Recognition for Social Media Texts with Semantic Augmentation](https://mlsanity.com/paper/2010.15458/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling](https://mlsanity.com/paper/2306.14806/) (2023)
- [Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling](https://mlsanity.com/paper/2310.15294/) (2023)
- [Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances](https://mlsanity.com/paper/2310.16790/) (2023)

## Abstract (arXiv)

Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios. Token substitution and mixup are two feasible heterogeneous self-augmentation techniques for NER that can achieve effective performance with certain specialized efforts. Noticeably, self-augmentation may introduce potentially noisy augmented data. Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually. In this paper, we revisit these two typical self-augmentation methods for NER, and propose a unified meta-reweighting strategy for them to achieve a natural integration. Our method is easily extensible, imposing little effort on a specific self-augmentation method. Experiments on different Chinese and English NER benchmarks show that our token substitution and mixup method, as well as their integration, can achieve effective performance improvement. Based on the meta-reweighting mechanism, we can enhance the advantages of the self-augmentation techniques without much extra effort.