https://github.com/zjunlp/low-resource-kepapers
A Paper List of Low-resource Information Extraction
https://github.com/zjunlp/low-resource-kepapers
artificial-intelligence awsome-list event-extraction few-shot-learning information-extraction knowledge-extraction knowledge-graph low-resource ner nlp paper paper-list relation-extraction survey
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A Paper List of Low-resource Information Extraction
- Host: GitHub
- URL: https://github.com/zjunlp/low-resource-kepapers
- Owner: zjunlp
- License: mit
- Created: 2022-01-29T10:01:20.000Z (over 3 years ago)
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- Last Pushed: 2024-11-16T15:55:34.000Z (11 months ago)
- Last Synced: 2024-11-16T16:34:09.179Z (11 months ago)
- Topics: artificial-intelligence, awsome-list, event-extraction, few-shot-learning, information-extraction, knowledge-extraction, knowledge-graph, low-resource, ner, nlp, paper, paper-list, relation-extraction, survey
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- Readme: README.md
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README
# Low-resource Information Extraction ๐
๐ The repository is a paper set on low-resource information extraction (IE), mainly including NER, RE and EE, which is generally categorized into two paradigms:
- **Traditional** Low-Resource IE approaches
- Exploiting Higher-Resource Data
- Developing Stronger Data-Efficient Models
- Optimizing Data and Models Together
- **LLM-based** Low-Resource IE approaches
- Direct Inference Without Tuning
- Model Specialization With Tuning
๐ค We strongly encourage the researchers who want to promote their fantastic work for the community to make pull request and update their papers in this repository!
๐ **Survey Paper**: Information Extraction in Low-Resource Scenarios: Survey and Perspective (ICKG 2024) \[[paper](https://arxiv.org/abs/2202.08063)\]
๐๏ธ **Slides**:
- Data-Efficient Knowledge Graph Construction, ้ซๆ็ฅ่ฏๅพ่ฐฑๆๅปบ ([Tutorial on CCKS 2022](http://sigkg.cn/ccks2022/?page_id=24)) \[[slides](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\]
- Efficient and Robust Knowledge Graph Construction ([Tutorial on AACL-IJCNLP 2022](https://www.aacl2022.org/Program/tutorials)) \[[paper](https://aclanthology.org/2022.aacl-tutorials.1.pdf), [slides](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\]
- Open-Environment Knowledge Graph Construction and Reasoning: Challenges, Approaches, and Opportunities ([Tutorial on IJCAI 2023](https://ijcai-23.org/tutorials/)) \[[slides](https://openkg-tutorial.github.io/)\]
## Content
[**Preliminaries**](#Preliminaries)
* [**๐ ๏ธ Low-Resource IE Toolkits**](#%EF%B8%8F-Low-Resource-IE-Toolkits)
* [Traditional Toolkits](#Traditional-Toolkits)
* [LLM-Based Toolkits](#LLM-Based-Toolkits)
* [**๐ Low-Resource IE Datasets**](#-Low-Resource-IE-Datasets)
* [Low-Resource NER](#Low-Resource-NER)
* [Low-Resource RE](#Low-Resource-RE)
* [Low-Resource EE](#Low-Resource-EE)
* [**๐ Related Surveys/Analysis on Low-Resource IE**](#-Related-Surveys-and-Analysis-on-Low-Resource-IE)
* [Information Extraction](#Information-Extraction)
* [Low-Resource NLP Learning](#Low-Resource-NLP-Learning)
[**๐Traditional Methods๐**](#-Traditional-Methods-)
* [**1. Exploiting Higher-Resource Data**](#1-Exploiting-Higher-Resource-Data)
* [1.1 Weakly Supervised Augmentation](#Weakly-Supervised-Augmentation)
* [1.2 Multimodal Augmentation](#Multimodal-Augmentation)
* [1.3 Multi-Lingual Augmentation](#Multi-Lingual-Augmentation)
* [1.4 Auxiliary Knowledge Enhancement](#Auxiliary-Knowledge-Enhancement)
* [**2. Developing Stronger Data-Efficient Models**](#2-Developing-Stronger-Data-Efficient-Models)
* [2.1 Meta Learning](#Meta-Learning)
* [2.2 Transfer Learning](#Transfer-Learning)
* [2.3 Fine-Tuning PLM](#Fine-Tuning-PLM)
* [**3. Optimizing Data and Models Together**](#3-Optimizing-Data-and-Models-Together)
* [3.1 Multi-Task Learning](#Multi-Task-Learning)
* [3.2 Task Reformulation](#Task-Reformulation)
* [3.3 Prompt-Tuning PLM](#Prompt-Tuning-PLM)
[**๐LLM-Based Methods๐**](#-LLM-Based-Methods-)
* [**Direct Inference Without Tuning**](#Direct-Inference-Without-Tuning)
* [Instruction Prompting](#Instruction-Prompting)
* [Code Prompting](#Code-Prompting)
* [In-Context Learning](#In-Context-Learning)
* [**Model Specialization With Tuning**](#Model-Specialization-With-Tuning)
* [Prompt-Tuning LLM](#Prompt-Tuning-LLM)
* [Fine-Tuning LLM](#Fine-Tuning-LLM)
[**How to Cite**](#How-to-Cite)
## Preliminaries
## ๐ ๏ธ Low-Resource IE Toolkits
### Traditional Toolkits
- DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population [[paper](https://aclanthology.org/2022.emnlp-demos.10/), [project](https://github.com/zjunlp/DeepKE)]
- OpenUE: An Open Toolkit of Universal Extraction from Text [[paper](https://aclanthology.org/2020.emnlp-demos.1.pdf), [project](https://github.com/zjunlp/OpenUE)]
- Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction [[paper](https://aclanthology.org/2023.acl-demo.34/), [project](https://github.com/IBM/zshot)]
- OpenNRE [[paper](https://aclanthology.org/D19-3029.pdf), [project](https://github.com/thunlp/OpenNRE)]
- OmniEvent [[paper1](https://aclanthology.org/2023.emnlp-demo.46.pdf), [paper2](https://aclanthology.org/2023.findings-acl.586.pdf), [project](https://github.com/THU-KEG/OmniEvent)]
### LLM-Based Toolkits
- CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction [[paper](https://arxiv.org/abs/2307.00769)]
- GPT4IE [[project](https://github.com/cocacola-lab/GPT4IE)]
- ChatIE [[paper](https://arxiv.org/abs/2302.10205), [project](https://github.com/cocacola-lab/ChatIE)]
- TechGPT: Technology-Oriented Generative Pretrained Transformer [[project](https://github.com/neukg/TechGPT)]
- TechGPT-2.0: A Large Language Model Project to Solve the Task of Knowledge Graph Construction [[paper](https://arxiv.org/abs/2401.04507), [project](https://github.com/neukg/TechGPT-2.0)]
- AutoKG: LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities [[paper](https://arxiv.org/abs/2305.13168), [project](https://github.com/zjunlp/AutoKG)]
- KnowLM [[project](https://github.com/zjunlp/KnowLM)]
## ๐ Low-Resource IE Datasets
### Low-Resource NER
* {***Few-NERD***}: Few-NERD: A Few-shot Named Entity Recognition Dataset (EMNLP 2021) \[[paper](https://aclanthology.org/2021.acl-long.248.pdf), [data](https://ningding97.github.io/fewnerd/)\]
### Low-Resource RE
* {***FewRel***}: FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (EMNLP 2018) \[[paper](https://aclanthology.org/D18-1514.pdf), [data](https://github.com/thunlp/FewRel)\]
* {***FewRel2.0***}: FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (EMNLP 2019) \[[paper](https://aclanthology.org/D19-1649.pdf), [data](https://github.com/thunlp/FewRel)\]
* {***Wiki-ZSL***}: ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (NAACL 2021) \[[paper](https://aclanthology.org/2021.naacl-main.272.pdf), [data](https://github.com/dinobby/ZS-BERT)\]
* {***Entail-RE***}: Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467), [data](https://github.com/231sm/Reasoning_In_KE)\]
* {***LREBench***}: Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (EMNLP 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-emnlp.29.pdf), [data](https://github.com/zjunlp/LREBench)\]
### Low-Resource EE
* {***FewEvent***}: Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection (WSDM 2020) \[[paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371796), [data](https://github.com/231sm/Low_Resource_KBP)\]
* {***Causal-EE***}: Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467), [data](https://github.com/231sm/Reasoning_In_KE)\]
* {***OntoEvent***}: OntoED: Low-resource Event Detection with Ontology Embedding (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.220.pdf), [data](https://github.com/231sm/Reasoning_In_EE)\]
* {***FewDocAE***}: Few-Shot Document-Level Event Argument Extraction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.446.pdf), [data](https://github.com/Xianjun-Yang/FewDocAE)\]
## ๐ Related Surveys and Analysis on Low-Resource IE
### Information Extraction
#### NER
* A Survey on Recent Advances in Named Entity Recognition from Deep Learning Models (COLING 2018) \[[paper](https://aclanthology.org/C18-1182.pdf)\]
* A Survey on Deep Learning for Named Entity Recognition (TKDE, 2020) \[[paper](https://ieeexplore.ieee.org/abstract/document/9039685)\]
* Few-Shot Named Entity Recognition: An Empirical Baseline Study (EMNLP 2021) \[[paper](https://aclanthology.org/2021.emnlp-main.813.pdf)\]
* Few-shot Named Entity Recognition: definition, taxonomy and research directions (TIST, 2023) \[[paper](https://dl.acm.org/doi/10.1145/3609483)\]
* Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges (arXiv, 2023) \[[paper](https://arxiv.org/abs/2309.14084)\]
* A Survey on Recent Advances in Named Entity Recognition (arXiv, 2024) \[[paper](https://arxiv.org/abs/2401.10825)\]
#### RE
* A Survey on Neural Relation Extraction (Science China Technological Sciences, 2020) \[[paper](https://link.springer.com/article/10.1007/s11431-020-1673-6)\]
* Relation Extraction: A Brief Survey on Deep Neural Network Based Methods (ICSIM 2021) \[[paper](https://dl.acm.org/doi/abs/10.1145/3451471.3451506)\]
* Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (TACL, 2021) \[[paper](https://aclanthology.org/2021.tacl-1.42.pdf)\]
* Deep Neural Network-Based Relation Extraction: An Overview (Neural Computing and Applications, 2022) \[[paper](https://link.springer.com/article/10.1007/s00521-021-06667-3)\]
* Revisiting Relation Extraction in the era of Large Language Models (ACL 2023) [[paper](https://aclanthology.org/2023.acl-long.868.pdf)\]
#### EE
* A Survey of Event Extraction From Text (ACCESS, 2019) \[[paper](https://ieeexplore.ieee.org/document/8918013)\]
* What is Event Knowledge Graph: A Survey (TKDE, 2022) \[[paper](https://ieeexplore.ieee.org/abstract/document/9792280)\]
* A Survey on Deep Learning Event Extraction: Approaches and Applications (TNNLS, 2022) \[[paper](https://ieeexplore.ieee.org/abstract/document/9927311)\]
* Event Extraction: A Survey (2022) [[paper](https://arxiv.org/abs/2210.03419)\]
* Low Resource Event Extraction: A Survey (2022) [[paper](https://www.cs.uoregon.edu/Reports/AREA-202210-Lai.pdf)\]
* Few-shot Event Detection: An Empirical Study and a Unified View (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.628.pdf)\]
* Exploring the Feasibility of ChatGPT for Event Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2303.03836)\]
* A Reevaluation of Event Extraction: Past, Present, and Future Challenges (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.09562)\]
* ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement (arXiv, 2023) [[paper](https://arxiv.org/abs/2401.13218)\]
#### General IE
**Traditional IE**
* From Information to Knowledge: Harvesting Entities and Relationships from Web Sources (PODS 2010) \[[paper](https://dl.acm.org/doi/abs/10.1145/1807085.1807097)\]
* Knowledge Base Population: Successful Approaches and Challenges (ACL 2011) \[[paper](https://aclanthology.org/P11-1115.pdf)\]
* Advances in Automated Knowledge Base Construction (NAACL-HLC 2012, AKBC-WEKEX workshop) \[[paper](https://www.semanticscholar.org/paper/Advances-in-Automated-Knowledge-Base-Construction-Suchanek/709e64be9cc9eb7c8b29bf49237cd2df835efd24)\]
* Information Extraction (IEEE Intelligent Systems, 2015) \[[paper](https://ieeexplore.ieee.org/abstract/document/7243219)\]
* Populating Knowledge Bases (Part of The Information Retrieval Series book series, 2018) \[[paper](https://link.springer.com/chapter/10.1007/978-3-319-93935-3_6)\]
* A Survey on Open Information Extraction (COLING 2018) \[[paper](https://aclanthology.org/C18-1326.pdf)\]
* A Survey on Automatically Constructed Universal Knowledge Bases (Journal of Information Science, 2020) \[[paper](https://journals.sagepub.com/doi/abs/10.1177/0165551520921342)\]
* Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (Foundations and Trends in Databases, 2021) [[paper](https://dl.acm.org/doi/10.1561/1900000064)\]
* A Survey on Knowledge Graphs: Representation, Acquisition and Applications (TNNLS, 2021) \[[paper](https://ieeexplore.ieee.org/document/9416312)\]
* Neural Symbolic Reasoning with Knowledge Graphs: Knowledge Extraction, Relational Reasoning, and Inconsistency Checking (Fundamental Research, 2021) \[[paper](https://www.sciencedirect.com/science/article/pii/S266732582100159X)\]
* A Survey on Neural Open Information Extraction: Current Status and Future Directions (IJCAI 2022) \[[paper](https://www.ijcai.org/proceedings/2022/0793.pdf)\]
* A Survey of Information Extraction Based on Deep Learning (Applied Sciences, 2022) \[[paper](https://www.mdpi.com/2076-3417/12/19/9691)\]
* Generative Knowledge Graph Construction: A Review (EMNLP 2022) \[[paper](https://aclanthology.org/2022.emnlp-main.1.pdf)\]
* Multi-Modal Knowledge Graph Construction and Application: A Survey (TKDE, 2022) \[[paper](https://ieeexplore.ieee.org/abstract/document/9961954)\]
* A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications (Mathematics, 2023) \[[paper](https://www.mdpi.com/2227-7390/11/8/1815)\]
* Construction of Knowledge Graphs: State and Challenges (Submitted to Semantic Web Journal, 2023) \[[paper](https://www.semantic-web-journal.net/content/construction-knowledge-graphs-state-and-challenges)\]
**LLM-based IE**
* Empirical Study of Zero-Shot NER with ChatGPT (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.493.pdf)\]
* Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.710.pdf)\]
* Evaluating ChatGPTโs Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness (arXiv, 2023) \[[paper](https://arxiv.org/abs/2304.11633)\]
* Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.14450)\]
* Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty (arXiv, 2023) \[[paper](https://arxiv.org/abs/2309.03433)\]
* LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2311.09366)\]
* LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.13168)\]
* Large Language Models for Generative Information Extraction: A Survey (arXiv, 2023) \[[paper](https://arxiv.org/abs/2312.17617)\]
* Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.96.pdf)\]
* LLMaAA: Making Large Language Models as Active Annotators (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.872.pdf)\]
* Large Language Models and Knowledge Graphs: Opportunities and Challenges (TGDK, 2023) \[[paper](https://drops.dagstuhl.de/storage/08tgdk/tgdk-vol001/tgdk-vol001-issue001/TGDK.1.1.2/TGDK.1.1.2.pdf)\]
* Unifying Large Language Models and Knowledge Graphs: A Roadmap (arXiv, 2023) \[[paper](https://arxiv.org/abs/2306.08302)\]
* Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications (arXiv, 2023) \[[paper](https://arxiv.org/abs/2311.05876)\]
* Large Knowledge Model: Perspectives and Challenges (arXiv, 2023) \[[paper](https://arxiv.org/abs/2312.02706)\]
* Knowledge Bases and Language Models: Complementing Forces (RuleML+RR, 2023) \[[paper](https://link.springer.com/chapter/10.1007/978-3-031-45072-3_1)\]
* StructGPT: A General Framework for Large Language Model to Reason over Structured Data (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.574.pdf)\]
* Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.360.pdf)\]
### Low-Resource NLP Learning
* A Survey of Zero-Shot Learning: Settings, Methods, and Applications (TIST, 2019) \[[paper](https://dl.acm.org/doi/10.1145/3293318)\]
* A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (NAACL 2021) \[[paper](https://aclanthology.org/2021.naacl-main.201.pdf)\]
* A Survey on Low-Resource Neural Machine Translation (IJCAI 2021) \[[paper](https://www.ijcai.org/proceedings/2021/0629.pdf)\]
* Generalizing from a Few Examples: A Survey on Few-shot Learning (ACM Computing Surveys, 2021) \[[paper](https://dl.acm.org/doi/10.1145/3386252)\]
* Knowledge-aware Zero-Shot Learning: Survey and Perspective (IJCAI 2021) \[[paper](https://www.ijcai.org/proceedings/2021/0597.pdf)\]
* Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs (IJCAI 2023) \[[paper](https://www.ijcai.org/proceedings/2023/0737.pdf)\]
* Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey (Proceedings of the IEEE, 2023) \[[paper](https://ieeexplore.ieee.org/document/10144560)\]
* A Survey on Machine Learning from Few Samples (Pattern Recognition, 2023) \[[paper](https://www.sciencedirect.com/science/article/pii/S0031320323001802)\]
* Multi-Hop Knowledge Graph Reasoning in Few-Shot Scenarios (TKDE, 2023) \[[paper](https://ieeexplore.ieee.org/document/10216353)\]
* An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (TACL, 2023) \[[paper](https://aclanthology.org/2023.tacl-1.12.pdf)\]
* Efficient Methods for Natural Language Processing: A Survey (TACL, 2023) \[[paper](https://aclanthology.org/2023.tacl-1.48.pdf)\]
## ๐ Traditional Methods ๐
## 1 Exploiting Higher-Resource Data
### Weakly Supervised Augmentation
* Distant Supervision for Relation Extraction without Labeled Data (ACL 2009) \[[paper](https://aclanthology.org/P09-1113.pdf)\]
* Modeling Missing Data in Distant Supervision for Information Extraction (TACL, 2013) \[[paper](https://aclanthology.org/Q13-1030.pdf)\]
* Neural Relation Extraction with Selective Attention over Instances (ACL 2016) \[[paper](https://aclanthology.org/P16-1200v2.pdf)\]
* Automatically Labeled Data Generation for Large Scale Event Extraction (ACL 2017) \[[paper](https://aclanthology.org/P17-1038.pdf)\]
* CoType: Joint Extraction of Typed Entities and Relations
with Knowledge Bases (WWW 2017) \[[paper](https://dl.acm.org/doi/abs/10.1145/3038912.3052708)\]
* Adversarial Training for Weakly Supervised Event Detection (NAACL 2019) \[[paper](https://aclanthology.org/N19-1105.pdf)\]
* Local Additivity Based Data Augmentation for Semi-supervised NER (EMNLP 2020) \[[paper](https://aclanthology.org/2020.emnlp-main.95/)\]
* BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision (KDD 2020) \[[paper](https://dl.acm.org/doi/abs/10.1145/3394486.3403149)\]
* Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (EMNLP 2021) \[[paper](https://aclanthology.org/2021.emnlp-main.216.pdf)\]
* Noisy-Labeled NER with Confidence Estimation (NAACL 2021) \[[paper](https://aclanthology.org/2021.naacl-main.269.pdf)\]
* ANEA: Distant Supervision for Low-Resource Named Entity Recognition (ICLR 2021, Workshop of Practical Machine Learning For Developing Countries) \[[paper](https://arxiv.org/pdf/2102.13129.pdf)\]
* Finding Influential Instances for Distantly Supervised Relation Extraction (COLING 2022) \[[paper](https://aclanthology.org/2022.coling-1.233.pdf)\]
* Better Sampling of Negatives for Distantly Supervised Named Entity Recognition (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.300.pdf)\]
* Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.813.pdf)\]
### Multimodal Augmentation
* Visual Attention Model for Name Tagging in Multimodal Social Media (ACL 2018) \[[paper](https://aclanthology.org/P18-1185.pdf)\]
* Visual Relation Extraction via Multi-modal Translation Embedding Based Model (PAKDD 2018) \[[paper](https://link.springer.com/chapter/10.1007/978-3-319-93034-3_43)\]
* Cross-media Structured Common Space for Multimedia Event Extraction (ACL 2020) \[[paper](https://aclanthology.org/2020.acl-main.230.pdf)\]
* Image Enhanced Event Detection in News Articles (AAAI 2020) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6437)\]
* Joint Multimedia Event Extraction from Video and Article (EMNLP 2021, Findings) \[[paper](https://aclanthology.org/2021.findings-emnlp.8.pdf)\]
* Multimodal Relation Extraction with Efficient Graph Alignment (MM 2021) \[[paper](https://dl.acm.org/doi/10.1145/3474085.3476968)\]
* Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion (SIGIR 2022) \[[paper](https://dl.acm.org/doi/pdf/10.1145/3477495.3531992)\]
* Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (NAACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-naacl.121.pdf)\]
### Multi-Lingual Augmentation
* Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields (IJCNLP 2017) \[[paper](https://aclanthology.org/I17-2016.pdf)\]
* Neural Relation Extraction with Multi-lingual Attention (ACL 2017) \[[paper](https://aclanthology.org/P17-1004.pdf)\]
* Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer (IJCAI 2018) \[[paper](https://www.ijcai.org/Proceedings/2018/0566.pdf)\]
* Event Detection via Gated Multilingual Attention Mechanism (AAAI 2018) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/11919)\]
* Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (COLING 2022) \[[paper](https://aclanthology.org/2022.coling-1.382.pdf)\]
* Cross-lingual Transfer Learning for Relation Extraction Using Universal Dependencies (Computer Speech & Language, 2022) \[[paper](https://www.sciencedirect.com/science/article/pii/S0885230821000711)\]
* Language Model Priming for Cross-Lingual Event Extraction (AAAI 2022) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21307)\]
* Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.317.pdf)\]
* PRAM: An End-to-end Prototype-based Representation Alignment Model for Zero-resource Cross-lingual Named Entity Recognition (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.201.pdf)\]
* Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.135.pdf)\]
* Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.296.pdf)\]
### Auxiliary Knowledge Enhancement
#### (1) Textual Knowledge (Type-related Knowledge & Synthesized Data)
* Zero-Shot Relation Extraction via Reading Comprehension (CoNLL 2017) \[[paper](https://aclanthology.org/K17-1034.pdf)\]
* Zero-Shot Open Entity Typing as Type-Compatible Grounding (EMNLP 2018) \[[paper](https://aclanthology.org/D18-1231.pdf)\]
* Description-Based Zero-shot Fine-Grained Entity Typing (NAACL 2019) \[[paper](https://aclanthology.org/N19-1087.pdf)\]
* Improving Event Detection via Open-domain Trigger Knowledge (ACL 2020) \[[paper](https://aclanthology.org/2020.acl-main.522.pdf)\]
* ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (NAACL 2021) \[[paper](https://aclanthology.org/2021.naacl-main.272.pdf)\]
* MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction (EMNLP 2021) \[[paper](https://aclanthology.org/2021.emnlp-main.212.pdf)\]
* Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (ACL 2021) \[[paper](https://aclanthology.org/P19-1429.pdf)\]
* MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.160.pdf)\]
* Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (EMNLP 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-emnlp.332.pdf)\]
* Low-Resource NER by Data Augmentation With Prompting (IJCAI 2022) [[paper](https://www.ijcai.org/proceedings/2022/0590.pdf)\]
* ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.8.pdf)\]
* Entity-to-Text based Data Augmentation for Various Named Entity Recognition Tasks (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.578.pdf)\]
* Improving Low-resource Named Entity Recognition with Graph Propagated Data Augmentation (ACL 2023, Short) \[[paper](https://aclanthology.org/2023.acl-short.11.pdf)\]
* GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.649.pdf)\]
* Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.727.pdf)\]
* RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.369.pdf)\]
* S2ynRE: Two-stage Self-training with Synthetic Data for Low-resource Relation Extraction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.455.pdf)\]
* Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.745.pdf)\]
* DAFS: A Domain Aware Few Shot Generative Model for Event Detection (Machine Learning, 2023) \[[paper](https://link.springer.com/article/10.1007/s10994-022-06198-5)\]
* Enhancing Few-shot NER with Prompt Ordering based Data Augmentation (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.11791)\]
* SegMix: A Simple Structure-Aware Data Augmentation Method (arXiv, 2023) \[[paper](https://arxiv.org/abs/2311.09505)\]
* Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.642.pdf)\]
* Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.334.pdf)\]
* STAR: Boosting Low-Resource Event Extraction by Structure-to-Text Data Generation with Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.15090)\]
* LLM-DA: Data Augmentation via Large Language Models for
Few-Shot Named Entity Recognition (arXiv, 2024) [[paper](https://arxiv.org/abs/2402.14568)\]
#### (2) Structured Knowledge (KG & Ontology & Logical Rules)
* Leveraging FrameNet to Improve Automatic Event Detection (ACL 2016) \[[paper](https://aclanthology.org/P16-1201.pdf)\]
* DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph (SIGIR 2021) \[[paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463113)\]
* Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (EMNLP 2020) \[[paper](https://aclanthology.org/2020.emnlp-main.50.pdf)\]
* Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (COLING 2020) \[[paper](https://aclanthology.org/2020.coling-main.265.pdf)\]
* NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (WWW 2020) \[[paper](https://dl.acm.org/doi/10.1145/3366423.3380282)\]
* Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity (AAAI 2021) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/17603)\]
* OntoED: Low-resource Event Detection with Ontology Embedding (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.220.pdf)\]
* Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467)\]
## 2 Developing Stronger Data-Efficient Models
### Meta Learning
#### For Low-Resource NER
* Few-shot Classification in Named Entity Recognition Task (SAC 2019) \[[paper](https://dl.acm.org/doi/10.1145/3297280.3297378)\]
* Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources (AAAI 2020) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/64667)\]
* MetaNER: Named Entity Recognition with Meta-Learning (WWW 2020) \[[paper](https://dl.acm.org/doi/10.1145/3366423.3380127)\]
* Meta-Learning for Few-Shot Named Entity Recognition (MetaNLP, 2021) \[[paper](https://aclanthology.org/2021.metanlp-1.6.pdf)\]
* Decomposed Meta-Learning for Few-Shot Named Entity Recognition (ACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-acl.124.pdf)\]
* Label Semantics for Few Shot Named Entity Recognition (ACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-acl.155.pdf)\]
* Few-Shot Named Entity Recognition via Meta-Learning (TKDE, 2022) \[[paper](https://doi.org/10.1109/TKDE.2020.3038670)\]
* Prompt-Based Metric Learning for Few-Shot NER (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.451.pdf)\]
* Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.203.pdf)\]
* HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition (CIKM 2023) \[[paper](https://dl.acm.org/doi/abs/10.1145/3583780.3614908)\]
* Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition (arXiv, 2023) \[[paper](https://arxiv.org/abs/2302.07739)\]
* Causal Interventions-based Few-Shot Named Entity Recognition (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.01914)\]
* MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging (arXiv, 2023) \[[paper](https://arxiv.org/abs/2108.11635)\]
#### For Low-Resource RE
* Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification (AAAI 2019) \[[paper](https://ojs.aaai.org//index.php/AAAI/article/view/4604)\]
* Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs (ICML 2020) \[[paper](http://proceedings.mlr.press/v119/qu20a/qu20a.pdf)\]
* Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (COLING 2020) \[[paper](https://aclanthology.org/2020.coling-main.563.pdf)\]
* Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (COLING 2020) \[[paper](https://aclanthology.org/2020.coling-main.140.pdf)\]
* Prototypical Representation Learning for Relation Extraction (ICLR 2021) \[[paper](https://openreview.net/forum?id=aCgLmfhIy_f)\]
* Pre-training to Match for Unified Low-shot Relation Extraction (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.397.pdf)\]
* Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (NAACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-naacl.139.pdf)\]
* fmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation (AAAI 2023) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26605)\]
* Interaction Information Guided Prototype Representation Rectification for Few-Shot Relation Extraction (Electronics, 2023) \[[paper](https://www.mdpi.com/2079-9292/12/13/2912)\]
* Consistent Prototype Learning for Few-Shot Continual Relation Extraction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.409.pdf)\]
* RAPL: A Relation-Aware Prototype Learning Approach for
Few-Shot Document-Level Relation Extraction (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.316.pdf)\]
* Density-Aware Prototypical Network for Few-Shot Relation Classification (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.162.pdf)\]
* Improving few-shot relation extraction through semantics-guided learning (Neural Networks, 2023) \[[paper](https://www.sciencedirect.com/science/article/pii/S0893608023006196)\]
* Generative Meta-Learning for Zero-Shot Relation Triplet Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.01920)\]
#### For Low-Resource EE
* Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection (WSDM 2020) \[[paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371796)\]
* Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (ACL 2021, Findings) \[[paper](https://aclanthology.org/2021.findings-acl.214.pdf)\]
* Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (ACL 2021, Findings) \[[paper](https://aclanthology.org/2021.findings-acl.3.pdf)\]
* Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.440.pdf)\]
* MultiPLe: Multilingual Prompt Learning for Relieving Semantic Confusions in Few-shot Event Detection (CIKM 2023) \[[paper](https://dl.acm.org/doi/abs/10.1145/3583780.3614984)\]
### Transfer Learning
* Zero-Shot Transfer Learning for Event Extraction (ACL 2018) \[[paper](https://aclanthology.org/P18-1201.pdf)\]
* Transfer Learning for Named-Entity Recognition with Neural Networks (LREC 2018) \[[paper](https://aclanthology.org/L18-1708.pdf)\]
* Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (NAACL 2019) \[[paper](https://aclanthology.org/N19-1306.pdf)\]
* Relation Adversarial Network for Low Resource Knowledge Graph Completion (WWW 2020) \[[paper](https://dl.acm.org/doi/pdf/10.1145/3366423.3380089)\]
* MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (COLING 2020) \[[paper](https://aclanthology.org/2020.coling-main.7.pdf)\]
* LearningToAdapt with Word Embeddings: Domain Adaptation of Named Entity Recognition Systems (Information Processing and Management, 2021) \[[paper](https://www.sciencedirect.com/science/article/pii/S0306457321000455)\]
* One Model for All Domains: Collaborative Domain-Prefx Tuning for Cross-Domain NER (IJCAI 2023) \[[paper](https://www.ijcai.org/proceedings/2023/0559.pdf)\]
* MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.234.pdf)\]
* Linguistic Representations for Fewer-shot Relation Extraction across Domains (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.414.pdf)\]
* Few-Shot Relation Extraction With Dual Graph Neural Network Interaction (TNNLS, 2023) \[[paper](https://ieeexplore.ieee.org/document/10143375)\]
* Leveraging Open Information Extraction for Improving Few-Shot Trigger Detection Domain Transfer (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.14163)\]
### Fine-Tuning PLM
* Matching the Blanks: Distributional Similarity for Relation Learning (ACL 2019) \[[paper](https://aclanthology.org/P19-1279.pdf)\]
* Exploring Pre-trained Language Models for Event Extraction and Generation (ACL 2019) \[[paper](https://aclanthology.org/P19-1522.pdf)\]
* Coarse-to-Fine Pre-training for Named Entity Recognition (EMNLP 2020) \[[paper](https://aclanthology.org/2020.emnlp-main.514.pdf)\]
* CLEVE: Contrastive Pre-training for Event Extraction (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.491.pdf)\]
* Unleash GPT-2 Power for Event Detection (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.490.pdf)\]
* Efficient Zero-shot Event Extraction with Context-Definition Alignment (EMNLP 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-emnlp.531.pdf)\]
* Few-shot Named Entity Recognition with Self-describing Networks (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.392.pdf)\]
* Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (ACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-acl.16.pdf)\]
* ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.183.pdf)\]
* Unleashing Pre-trained Masked Language Model Knowledge for Label Signal Guided Event Detection (DASFAA 2023) \[[paper](https://link.springer.com/chapter/10.1007/978-3-031-30675-4_42)\]
* A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER (CIKM 2023) \[[paper](https://dl.acm.org/doi/10.1145/3583780.3614766)\]
* Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.739.pdf)\]
* Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.433.pdf)\]
* GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer (arXiv, 2023) \[[paper](https://arxiv.org/abs/2311.08526)\]
* Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2312.12021)\]
## 3 Optimizing Data and Models Together
### Multi-Task Learning
#### (1) IE & IE-Related Tasks
**NER, Named Entity Normalization (NEN)**
* A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization (AAAI 2019) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/3861)\]
* MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization (AAAI 2021) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/17714)\]
* An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.485.pdf)\]
**Word Sense Disambiguation (WSD), Event Detection (ED)**
* Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (EMNLP 2018) \[[paper](https://aclanthology.org/D18-1517.pdf)\]
* Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection (SIGIR 2021) \[[paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463054)\]
#### (2) Joint IE & Other Structured Prediction Tasks
**NER, RE**
* GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (ACL 2019) \[[paper](https://aclanthology.org/P19-1136.pdf)\]
* CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning (AAAI 2020) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6495)\]
* Joint Entity and Relation Extraction Model based on Rich Semantics (Neurocomputing, 2021) \[[paper](https://www.sciencedirect.com/science/article/pii/S0925231220319378?casa_token=jzgLW9J1UKoAAAAA:5vnqqGKt0_-ykbhTp15Bq8mB-8B50cM3LDa10q2h8yc4q4AJVfeEbQV_fyMo2Z92xjl3HPNt6w)\]
**NER, RE, EE**
* Entity, Relation, and Event Extraction with Contextualized Span Representations (EMNLP 2019) \[[paper](https://aclanthology.org/D19-1585.pdf)\]
**NER, RE, EE & Other Structured Prediction Tasks**
* SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.21.pdf)\]
* Mirror: A Universal Framework for Various Information Extraction Tasks (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.548.pdf)\]
### Task Reformulation
* Zero-Shot Relation Extraction via Reading Comprehension (CoNLL 2017) \[[paper](https://aclanthology.org/K17-1034.pdf)\]
* Entity-Relation Extraction as Multi-Turn Question Answering (ACL 2019) \[[paper](http://aclanthology.lst.uni-saarland.de/P19-1129.pdf)\]
* A Unified MRC Framework for Named Entity Recognition (ACL 2020) \[[paper](https://aclanthology.org/2020.acl-main.519.pdf)\]
* Event Extraction as Machine Reading Comprehension (EMNLP 2020) \[[paper](https://aclanthology.org/2020.emnlp-main.128.pdf)\]
* Event Extraction by Answering (Almost) Natural Questions (EMNLP 2020) \[[paper](https://aclanthology.org/2020.emnlp-main.49.pdf)\]
* Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.217.pdf)\]
* Structured Prediction as Translation between Augmented Natural Languages (ICLR 2021) \[[paper](https://openreview.net/forum?id=US-TP-xnXI)\]
* A Unified Generative Framework for Various NER Subtasks (ACL 2021) \[[paper](https://aclanthology.org/2021.acl-long.451.pdf)\]
* REBEL: Relation Extraction By End-to-end Language Generation (EMNLP 2021, Findings) \[[paper](https://aclanthology.org/2021.findings-emnlp.204.pdf)\]
* GenIE: Generative Information Extraction (NAACL 2022) \[[paper](https://aclanthology.org/2022.naacl-main.342.pdf)\]
* Learning to Ask for Data-Efficient Event Argument Extraction (AAAI 2022, Student Abstract) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21686)\]
* Complex Question Enhanced Transfer Learning for Zero-shot Joint Information Extraction (TASLP, 2023) \[[paper](https://ieeexplore.ieee.org/abstract/document/10214665)\]
* Weakly-Supervised Questions for Zero-Shot Relation Extraction (EACL 2023) \[[paper](https://aclanthology.org/2023.eacl-main.224.pdf)\]
* Event Extraction as Question Generation and Answering (ACL 2023, Short) \[[paper](https://aclanthology.org/2023.acl-short.143.pdf)\]
* Set Learning for Generative Information Extraction (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.806.pdf)\]
### Prompt-Tuning PLM
#### (1) Vanilla Prompt-Tuning
* Template-Based Named Entity Recognition Using BART (ACL 2021, Findings) \[[paper](https://aclanthology.org/2021.findings-acl.161.pdf)\]
* Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (EMNLP 2021) \[[paper](https://aclanthology.org/2021.emnlp-main.92.pdf)\]
* LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (COLING 2022) \[[paper](https://aclanthology.org/2022.coling-1.209.pdf)\]
* COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (COLING 2022) \[[paper](https://aclanthology.org/2022.coling-1.222.pdf)\]
* Template-free Prompt Tuning for Few-shot NER (NAACL 2022) \[[paper](https://aclanthology.org/2022.naacl-main.420.pdf)\]
* Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (NAACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-naacl.187.pdf)\]
* RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (ACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-acl.5.pdf)\]
* Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.466.pdf)\]
* Dynamic Prefix-Tuning for Generative Template-based Event Extraction (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.358.pdf)\]
* Good Examples Make A Faster Learner Simple Demonstration-based Learning for Low-resource NER (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.192.pdf)\]
* Prompt-Learning for Cross-Lingual Relation Extraction (IJCNN 2023) \[[paper](https://arxiv.org/abs/2304.10354)\]
* DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.339.pdf)\]
* Contextualized Soft Prompts for Extraction of Event Arguments (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.266.pdf)\]
* The Art of Prompting: Event Detection based on Type Specific Prompts (ACL 2023, Short) \[[paper](https://aclanthology.org/2023.acl-short.111.pdf)\]
* Prompt for Extraction: Multiple Templates Choice Model for Event Extraction (KBS, 2024) \[[paper](https://www.sciencedirect.com/science/article/pii/S0950705124001795)\]
* UMIE: Unified Multimodal Information Extraction with Instruction Tuning (arXiv, 2024) \[[paper](https://arxiv.org/abs/2401.03082)\]
#### (2) Augmented Prompt-Tuning
* PTR: Prompt Tuning with Rules for Text Classification (AI Open, 2022) \[[paper](https://www.sciencedirect.com/science/article/pii/S2666651022000183)\]
* KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction (WWW 2022) \[[paper](https://dl.acm.org/doi/abs/10.1145/3485447.3511998)\]
* Ontology-enhanced Prompt-tuning for Few-shot Learning (WWW 2022) \[[paper](https://dl.acm.org/doi/10.1145/3485447.3511921)\]
* Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning (SIGIR 2022, Short) \[[paper](https://dl.acm.org/doi/abs/10.1145/3477495.3531746)\]
* Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning (NeurIPS 2022) \[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/97011c648eda678424f9292dadeae72e-Paper-Conference.pdf)\]
* AugPrompt: Knowledgeable Augmented-Trigger Prompt for Few-Shot Event Classification (Information Processing & Management, 2022) \[[paper](https://www.sciencedirect.com/science/article/abs/pii/S0306457322002540)\]
* Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (NAACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-naacl.196.pdf)\]
* DEGREE: A Data-Efficient Generation-Based Event Extraction Model (NAACL 2022) \[[paper](https://aclanthology.org/2022.naacl-main.138.pdf)\]
* Retrieval-Augmented Generative Question Answering for Event Argument Extraction (EMNLP 2022) \[[paper](https://aclanthology.org/2022.emnlp-main.307.pdf)\]
* Unified Structure Generation for Universal Information Extraction (ACL 2022) \[[paper](https://aclanthology.org/2022.acl-long.395.pdf)\]
* LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model (NeurIPS 2022) \[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/63943ee9fe347f3d95892cf87d9a42e6-Abstract-Conference.html)\]
* Universal Information Extraction as Unified Semantic Matching (AAAI 2023) \[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26563)\]
* Universal Information Extraction with Meta-Pretrained Self-Retrieval (ACL 2023) \[[paper](https://aclanthology.org/2023.findings-acl.251.pdf)\]
* RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.1024.pdf)\]
* Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction (SIGIR 2023) \[[paper](https://arxiv.org/abs/2210.10709)\]
* PromptNER: Prompt Locating and Typing for Named Entity Recognition (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.698.pdf)\]
* Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.164.pdf)\]
* Revisiting Relation Extraction in the era of Large Language Models (ACL 2023) [[paper](https://aclanthology.org/2023.acl-long.868.pdf)\]
* AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.615.pdf)\]
* BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.309.pdf)\]
* Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.17.pdf)\]
* Easy-to-Hard Learning for Information Extraction (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.754.pdf)\]
* DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.121.pdf)\]
* 2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.259.pdf)\]
* Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive Learning (TNNLS, 2023) \[[paper](https://ieeexplore.ieee.org/abstract/document/10264144)\]
* TaxonPrompt: Taxonomy-Aware Curriculum Prompt Learning for Few-Shot Event Classification (KBS, 2023) \[[paper](https://www.sciencedirect.com/science/article/pii/S0950705123000400)\]
* A Composable Generative Framework based on Prompt Learning for Various Information Extraction Tasks (IEEE Transactions on Big Data, 2023) \[[paper](https://ieeexplore.ieee.org/abstract/document/10130644)\]
* Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval (TKDE, 2023) \[[paper](https://doi.org/10.1109/TKDE.2023.3266495)\]
* MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection (Information Processing & Management, 2023) \[[paper](https://www.sciencedirect.com/science/article/abs/pii/S0306457323002467)\]
* PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.12217)\]
* TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition (arXiv, 2023) \[[paper](https://arxiv.org/abs/2306.03974)\]
* OntoType: Ontology-Guided Zero-Shot Fine-Grained Entity Typing with Weak Supervision from Pre-Trained Language Models (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.12307)\]
## ๐ LLM-Based Methods ๐
## Direct Inference Without Tuning
### Instruction Prompting
* Exploring the Feasibility of ChatGPT for Event Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2303.03836)\]
* Zero-Shot Information Extraction via Chatting with ChatGPT (arXiv, 2023) \[[paper](https://arxiv.org/abs/2302.10205)\]
* Global Constraints with Prompting for Zero-Shot Event Argument Classification (EACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-eacl.191.pdf)\]
* Revisiting Large Language Models as Zero-shot Relation Extractors (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.459.pdf)\]
* Empirical Study of Zero-Shot NER with ChatGPT (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.493.pdf)\]
* AutoKG: Efficient Automated Knowledge Graph Generation for Language Models (IEEE BigData 2023, GTA3 Workshop) \[[paper](https://arxiv.org/abs/2311.14740)\]
* PromptNER : Prompting For Named Entity Recognition (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.15444)\]
* Zero-shot Temporal Relation Extraction with ChatGPT (ACL 2023, BioNLP) \[[paper](https://aclanthology.org/2023.bionlp-1.7.pdf)\]
* Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness (arXiv, 2023) \[[paper](https://arxiv.org/abs/2304.11633)\]
* LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.13168)\]
* Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.11142)\]
* A Simple but Effective Approach to Improve Structured Language Model
Output for Information Extraction (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.13364)\]
### Code Prompting
* Code4Struct: Code Generation for Few-Shot Event Structure Prediction (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.202.pdf)\]
* CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.855.pdf)\]
* ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.824.pdf)\]
* Retrieval-Augmented Code Generation for Universal Information Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.02962)\]
* CodeKGC: Code Language Model for Generative Knowledge Graph Construction (arXiv, 2023) [[paper](https://arxiv.org/abs/2304.09048)\]
* GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2310.03668)\]
### In-Context Learning
* Learning In-context Learning for Named Entity Recognition (ACL 2023) \[[paper](https://aclanthology.org/2023.acl-long.764.pdf)\]
* How to Unleash the Power of Large Language Models for Few-shot Relation Extraction? (ACL 2023, SustaiNLP Workshop) [[paper](https://aclanthology.org/2023.sustainlp-1.13.pdf)\]
* GPT-RE: In-context Learning for Relation Extraction using Large Language Models (EMNLP 2023) [[paper](https://aclanthology.org/2023.emnlp-main.214.pdf)\]
* In-context Learning for Few-shot Multimodal Named Entity Recognition (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.196.pdf)\]
* Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.184.pdf)\]
* Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.710.pdf)\]
* Guideline Learning for In-Context Information Extraction (EMNLP 2023) [[paper](https://aclanthology.org/2023.emnlp-main.950.pdf)\]
* Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (EMNLP 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-emnlp.153.pdf)\]
* Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction (IALP 2023) [[paper](https://ieeexplore.ieee.org/document/10337264)\]
* GPT-NER: Named Entity Recognition via Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2304.10428)\]
* In-Context Few-Shot Relation Extraction via Pre-Trained Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2310.11085)\]
* Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.08921)\]
* Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors (arXiv, 2023) \[[paper](https://arxiv.org/abs/2305.14450)\]
* GPT Struct Me: Probing GPT Models on Narrative Entity Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.14583)\]
* Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty (arXiv, 2023) \[[paper](https://arxiv.org/abs/2309.03433)\]
* LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2311.09366)\]
* Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models (arXiv, 2023) \[[paper](https://arxiv.org/abs/2312.01954)\]
* Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2306.14122v3)\]
* Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (COLING 2024) \[[paper](https://arxiv.org/abs/2402.13741)\]
* LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.10573)\]
* GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.10744)\]
* C-ICL: Contrastive In-context Learning for Information Extraction (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.11254)\]
* EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.11430)\]
* Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.14373)\]
## Model Specialization With Tuning
### Prompt-Tuning LLM
* DeepStruct: Pretraining of Language Models for Structure Prediction (ACL 2022, Findings) \[[paper](https://aclanthology.org/2022.findings-acl.67v2.pdf)\]
* Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (ACL 2023, Findings) \[[paper](https://aclanthology.org/2023.findings-acl.50.pdf)\]
* Instruct and Extract: Instruction Tuning for On-Demand Information Extraction (EMNLP 2023) \[[paper](https://aclanthology.org/2023.emnlp-main.620.pdf)\]
* UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition (ICLR 2024) [[paper](https://openreview.net/forum?id=r65xfUb76p)\]
* InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2304.08085)\]
* YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction (arXiv, 2023) \[[paper](https://arxiv.org/abs/2312.15548)\]
* ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models (COLING 2024) \[[paper](https://arxiv.org/abs/2403.05132)\]
### Fine-Tuning LLM
* Fine-Tuning GPT Family (OpenAI, 2023) \[[Documentation](https://platform.openai.com/docs/guides/fine-tuning)\]
* EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models (arXiv, 2024) \[[paper](https://arxiv.org/abs/2402.11430)\]
## How to Cite
๐ Thank you very much for your interest in our survey work. If you use or extend our survey, please cite the following paper:
```bibtex
@misc{2023_LowResIE,
author = {Shumin Deng and
Yubo Ma and
Ningyu Zhang and
Yixin Cao and
Bryan Hooi},
title = {Information Extraction in Low-Resource Scenarios: Survey and Perspective},
journal = {CoRR},
volume = {abs/2202.08063},
year = {2023},
url = {https://arxiv.org/abs/2202.08063}
}
```