# An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling

arXiv: https://arxiv.org/abs/2109.13023 · HTML: https://mlsanity.com/paper/2109.13023/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning](https://mlsanity.com/paper/1905.06549/) (2019)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [Few-shot classification in Named Entity Recognition Task](https://mlsanity.com/paper/1812.06158/) (2018)
- [Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/1902.10482/) (2019)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [Augmented Natural Language for Generative Sequence Labeling](https://mlsanity.com/paper/2009.13272/) (2020)
- [ConVEx: Data-Efficient and Few-Shot Slot Labeling](https://mlsanity.com/paper/2010.11791/) (2020)
- [Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification](https://mlsanity.com/paper/2108.12844/) (2021)

## Built on by (most cited first, 26 of 26)

- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [Prompt-Based Metric Learning for Few-Shot NER](https://mlsanity.com/paper/2211.04337/) (2022)
- [VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna](https://mlsanity.com/paper/2305.03253/) (2023)
- [A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER](https://mlsanity.com/paper/2308.14533/) (2023)
- [SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.09049/) (2022)
- [UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective](https://mlsanity.com/paper/2305.10306/) (2023)
- [Learning In-context Learning for Named Entity Recognition](https://mlsanity.com/paper/2305.11038/) (2023)
- [A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition](https://mlsanity.com/paper/2302.13610/) (2023)
- [PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search](https://mlsanity.com/paper/2305.12217/) (2023)
- [Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2302.06397/) (2023)
- [Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification](https://mlsanity.com/paper/2312.07961/) (2023)
- [A Unified Framework for Multi-intent Spoken Language Understanding with prompting](https://mlsanity.com/paper/2210.03337/) (2022)
- [In-Context Learning for Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2402.01182/) (2024)
- [Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition](https://mlsanity.com/paper/2406.05460/) (2024)
- [BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2412.02228/) (2024)
- [MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation](https://mlsanity.com/paper/2505.03827/) (2025)
- [Few-shot Classification with Hypersphere Modeling of Prototypes](https://mlsanity.com/paper/2211.05319/) (2022)
- [Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2212.00953/) (2022)
- [Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2403.14222/) (2024)
- [Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2302.07739/) (2023)
- [Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2307.07946/) (2023)
- [Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets](https://mlsanity.com/paper/2310.11715/) (2023)
- [Few-shot Named Entity Recognition via Superposition Concept Discrimination](https://mlsanity.com/paper/2403.16463/) (2024)
- [Causal Interventions-based Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2305.01914/) (2023)
- [Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning](https://mlsanity.com/paper/2404.06970/) (2024)

## Abstract (arXiv)

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the nested and noisy tagging scenarios. Our code is available at https://github.com/Wangpeiyi9979/ESD.