---
title: "RelationPrompt — paper by Chia et al. (2022)"
description: "This paper introduces Zero-Shot Relation Triplet Extraction (ZeroRTE). 43 references, cited by 50 papers here."
authors: ["Yew Ken Chia", "Lidong Bing", "Soujanya Poria", "Luo Si"]
year: 2022
venue: "Findings of the Association for Computational Linguistics: ACL 2022"
source: "https://aclanthology.org/2022.findings-acl.5/"
doi: "10.18653/v1/2022.findings-acl.5"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/) / RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

# RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

**Authors:** [Yew Ken Chia](https://md.research.elamur.ai/free/en/authors/yew-ken-chia/), [Lidong Bing](https://md.research.elamur.ai/free/en/authors/lidong-bing/), [Soujanya Poria](https://md.research.elamur.ai/free/en/authors/soujanya-poria/) and [Luo Si](https://md.research.elamur.ai/free/en/authors/luo-si/) · **Year:** 2022 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2022](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2022](https://md.research.elamur.ai/free/en/venues/findings/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.findings-acl.5/) · [PDF](https://aclanthology.org/2022.findings-acl.5.pdf) · [DOI 10.18653/v1/2022.findings-acl.5](https://doi.org/10.18653/v1/2022.findings-acl.5) · [OpenAlex](https://openalex.org/W4226278302)

**Citations:** 91 citations (OpenAlex, 2026-10-14) · [50](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most relation extraction systems need large annotated datasets and only handle a fixed set of relation types. This paper introduces Zero-Shot Relation Triplet Extraction (ZeroRTE). In this setting, a model must pull complete (head entity, tail entity, relation label) triplets out of raw sentences. The relation labels at test time never appear in training. Earlier zero-shot settings assumed gold entities or a given head entity, so ZeroRTE is the harder task.

The authors' framework, RelationPrompt, treats the problem as data synthesis. A language model is fine-tuned to act as a Relation Generator. It receives a relation label as a prompt and writes a sentence plus an entity pair in a structured text format. A separate Relation Extractor is then trained on this synthetic data for the unseen labels. Each generated sentence holds only one triplet, so the authors add Triplet Search Decoding to recover several triplets per sentence at inference.

On reorganized FewRel and Wiki-ZSL, RelationPrompt beats prior zero-shot relation classification (ZeroRC) methods and ZeroRTE baselines. The work matters because it gives a benchmark and a reference point for low-resource relation extraction.

## Key points
- **New task:** ZeroRTE is presented as the first setting that extracts full relation triplets with no annotated examples of the test relations. The seen and unseen label sets are disjoint.
- **Benchmark data:** The benchmark is built from reorganized FewRel and Wiki-ZSL. Code and data are released at github.com/declare-lab/RelationPrompt.
- **Structured prompt templates:**
  - The generator maps "Relation: \<Label>" to a context sentence plus head and tail entities.
  - The extractor maps a sentence to head, tail and relation.
  - The same extractor also supports ZeroRC.
- **Triplet Search Decoding:** This method lets an extractor trained only on single-triplet synthetic samples output multiple triplets. The authors describe it as effective and interpretable.
- **Analysis and limitations:**
  - Generated samples are judged reasonable and diverse.
  - Performance is also studied as a function of the amount of synthetic data per label.
  - A noted weakness is that generated entity spans sometimes fit the prompted relation poorly.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) — University, Singapore, Singapore · 33 works · 10,760 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 1,282 works

## Cite

Cite as: Yew Ken Chia, Lidong Bing, Soujanya Poria and Luo Si. 2022. RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction. Findings of the Association for Computational Linguistics: ACL 2022.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chia2022relationprompt,
  title = {{RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction}},
  author = {Yew Ken Chia, Lidong Bing, Soujanya Poria and Luo Si},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-acl.5},
  url = {https://doi.org/10.18653/v1/2022.findings-acl.5}
}
```

- [← previous IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://md.research.elamur.ai/free/en/papers/dabre-2022-indicbart-pre-trained-model-indic/)
- [next → A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/)

## References

43 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. 2020. [Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proc. of ACL.
2. Rami Aly, Andreas Vlachos, and Ryan McDonald. 2021. Leveraging type descriptions for zero-shot named entity recognition and classification. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Rami+Aly%2C+Andreas+Vlachos%2C+and+Ryan+McDonald%2E+2021%2E+Leveraging+type+descriptions+for+zero%2Dshot+named+entity+recognition+and+classification%2E+In+Proc%2E+of+ACL%2E)
3. Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019. [Entity-relation extraction as multi-turn question answering](https://md.research.elamur.ai/free/en/papers/li-2019-entity-relation-extraction-as-multi/). In Proc. of ACL.
4. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2001. [A neural probabilistic language model](https://md.research.elamur.ai/free/en/papers/bengio-2003-neural-probabilistic-language-model/). In Proc. of NeurIPS.
5. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Yankai+Lin%2C+Zhiyuan+Liu%2C+Maosong+Sun%2C+Yang+Liu%2C+and+Xuan+Zhu%2E+2015%2E+Learning+entity+and+relation+embeddings+for+knowledge+graph+completion%2E+In+Proc%2E+of+AAAI%2E)
6. Chih-Yao Chen and Cheng-Te Li. 2021. [Zs-bert: Towards zero-shot relation extraction with attribute representation learning](https://md.research.elamur.ai/free/en/papers/chen-2021-zs-bert-zero-shot-relation/). In Proc. of NAACL.
7. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021. [Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/). CoRR, arXiv:2107.13586.
8. Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021. [Template-based named entity recognition using BART](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/). In Findings of ACL-IJCNLP.
9. Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, and Chunyan Miao. 2020. [DAGA: Data augmentation with a generation approach for low-resource tagging tasks](https://md.research.elamur.ai/free/en/papers/ding-2020-daga-data-augmentation-generation-approach/). In Proc. of EMNLP.
10. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In Proc. of ICLR. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2019%2E+Decoupled+weight+decay+regularization%2E+In+Proc%2E+of+ICLR%2E)
11. Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022. [Generating training data with language models: Towards zero-shot language understanding](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/). CoRR, arXiv:2202.04538.
12. Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018. Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Xu+Han%2C+Hao+Zhu%2C+Pengfei+Yu%2C+Ziyun+Wang%2C+Yuan+Yao%2C+Zhiyuan+Liu%2C+and+Maosong+Sun%2E+2018%2E+Fewrel%3A+A+large%2Dscale+supervised+few%2Dshot+relation+classification+dataset+with+state%2Dof%2Dthe%2Dart+evaluation%2E+In+Pr)
13. Tapas Nayak and Hwee Tou Ng. 2020. Effective modeling of encoder-decoder architecture for joint entity and relation extraction. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Tapas+Nayak+and+Hwee+Tou+Ng%2E+2020%2E+Effective+modeling+of+encoder%2Ddecoder+architecture+for+joint+entity+and+relation+extraction%2E+In+Proc%2E+of+AAAI%2E)
14. Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. [Distilling the knowledge in a neural network](https://md.research.elamur.ai/free/en/papers/hinton-2015-distilling-knowledge-neural-network/). CoRR, abs/1503.02531.
15. Abiola Obamuyide and Andreas Vlachos. 2018. Zero-shot relation classification as textual entailment. In Proc. of FEVER. [find](https://scholar.google.com/scholar?q=Abiola+Obamuyide+and+Andreas+Vlachos%2E+2018%2E+Zero%2Dshot+relation+classification+as+textual+entailment%2E+In+Proc%2E+of+FEVER%2E)
16. Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, and Clare Voss. 2018. [Zero-shot transfer learning for event extraction](https://md.research.elamur.ai/free/en/papers/huang-2018-zero-shot-transfer-learning-event/). In Proc. of ACL.
17. Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos Santos, Bing Xiang, and Stefano Soatto. 2020. Structured prediction as translation between augmented natural languages. In Proc. of ICLR. [find](https://scholar.google.com/scholar?q=Giovanni+Paolini%2C+Ben+Athiwaratkun%2C+Jason+Krone%2C+Jie+Ma%2C+Alessandro+Achille%2C+Rishita+Anubhai%2C+Cicero+Nogueira+dos+Santos%2C+Bing+Xiang%2C+and+Stefano+Soatto%2E+2020%2E+Structured+prediction+as+translation+bet)
18. Guoliang Ji, Kang Liu, Shizhu He, and Jun Zhao. 2017. Distant supervision for relation extraction with sentence-level attention and entity descriptions. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Guoliang+Ji%2C+Kang+Liu%2C+Shizhu+He%2C+and+Jun+Zhao%2E+2017%2E+Distant+supervision+for+relation+extraction+with+sentence%2Dlevel+attention+and+entity+descriptions%2E+In+Proc%2E+of+AAAI%2E)
19. Yannis Papanikolaou and Andrea Pierleoni. 2020. DARE: data augmented relation extraction with GPT-2. CoRR, arXiv:2004.13845. — [arXiv:2004.13845](https://arxiv.org/abs/2004.13845) [find](https://scholar.google.com/scholar?q=Yannis+Papanikolaou+and+Andrea+Pierleoni%2E+2020%2E+DARE%3A+data+augmented+relation+extraction+with+GPT%2D2%2E+CoRR%2C+arXiv%3A2004%2E13845%2E+%E2%80%94+%5BarXiv%3A2004%2E13845%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2004%2E13845%29)
20. Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019. [Ctrl: A conditional transformer language model for controllable generation](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/). CoRR, arXiv:1909.05858.
21. Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019. [Language models as knowledge bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) In Proc. of EMNLP-IJCNLP.
22. Sosuke Kobayashi. 2018. [Contextual augmentation: Data augmentation by words with paradigmatic relations](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/). In Proc. of NAACL.
23. Raul Puri and Bryan Catanzaro. 2019. Zero-shot text classification with generative language models. CoRR, arXiv:1912.10165. — [arXiv:1912.10165](https://arxiv.org/abs/1912.10165) [find](https://scholar.google.com/scholar?q=Raul+Puri+and+Bryan+Catanzaro%2E+2019%2E+Zero%2Dshot+text+classification+with+generative+language+models%2E+CoRR%2C+arXiv%3A1912%2E10165%2E+%E2%80%94+%5BarXiv%3A1912%2E10165%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1912%2E10165%29)
24. Kenton Lee, Kelvin Guu, Luheng He, Tim Dozat, and Hyung Won Chung. 2021. Neural data augmentation via example extrapolation. CoRR, arXiv:2102.01335. — [arXiv:2102.01335](https://arxiv.org/abs/2102.01335) [find](https://scholar.google.com/scholar?q=Kenton+Lee%2C+Kelvin+Guu%2C+Luheng+He%2C+Tim+Dozat%2C+and+Hyung+Won+Chung%2E+2021%2E+Neural+data+augmentation+via+example+extrapolation%2E+CoRR%2C+arXiv%3A2102%2E01335%2E+%E2%80%94+%5BarXiv%3A2102%2E01335%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2102%2E013)
25. Pushpankar Kumar Pushp and Muktabh Mayank Srivastava. 2017. Train once, test anywhere: Zero-shot learning for text classification. CoRR, arXiv:1712.05972. — [arXiv:1712.05972](https://arxiv.org/abs/1712.05972) [find](https://scholar.google.com/scholar?q=Pushpankar+Kumar+Pushp+and+Muktabh+Mayank+Srivastava%2E+2017%2E+Train+once%2C+test+anywhere%3A+Zero%2Dshot+learning+for+text+classification%2E+CoRR%2C+arXiv%3A1712%2E05972%2E+%E2%80%94+%5BarXiv%3A1712%2E05972%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F17)
26. Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. In Proc. of CoNLL. [find](https://scholar.google.com/scholar?q=Omer+Levy%2C+Minjoon+Seo%2C+Eunsol+Choi%2C+and+Luke+Zettlemoyer%2E+2017%2E+Zero%2Dshot+relation+extraction+via+reading+comprehension%2E+In+Proc%2E+of+CoNLL%2E)
27. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jeffrey+Wu%2C+Rewon+Child%2C+David+Luan%2C+Dario+Amodei%2C+Ilya+Sutskever%2C+et+al%2E+2019%2E+Language+models+are+unsupervised+multitask+learners%2E+OpenAI%2E)
28. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. [find](https://scholar.google.com/scholar?q=Mike+Lewis%2C+Yinhan+Liu%2C+Naman+Goyal%2C+Marjan+Ghazvininejad%2C+Abdelrahman+Mohamed%2C+Omer+Levy%2C+Veselin+Stoyanov%2C+and+Luke+Zettlemoyer%2E)
29. Laria Reynolds and Kyle McDonell. 2021. [Prompt programming for large language models: Beyond the few-shot paradigm](https://md.research.elamur.ai/free/en/papers/reynolds-2021-prompt-programming-large-language-models/). In Proc. of CHI.
30. Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How much knowledge can you pack into the parameters of a language model? In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Adam+Roberts%2C+Colin+Raffel%2C+and+Noam+Shazeer%2E+2020%2E+How+much+knowledge+can+you+pack+into+the+parameters+of+a+language+model%3F+In+Proc%2E+of+EMNLP%2E)
31. Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, and Phil Blunsom. 2016. [Reasoning about entailment with neural attention](https://md.research.elamur.ai/free/en/papers/rocktaschel-2015-reasoning-about-entailment-neural-attention/). In Proc. of ICLR.
32. Alisa Smirnova and Philippe Cudré-Mauroux. 2018. Relation extraction using distant supervision: A survey. CSUR, 51(5):1:35. [find](https://scholar.google.com/scholar?q=Alisa+Smirnova+and+Philippe+Cudr%C3%A9%2DMauroux%2E+2018%2E+Relation+extraction+using+distant+supervision%3A+A+survey%2E+CSUR%2C+51%285%29%3A1%3A35%2E)
33. Jue Wang and Wei Lu. 2020. [Two are better than one: Joint entity and relation extraction with table-sequence encoders](https://md.research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/). In Proc. of EMNLP.
34. Wei Wang, Vincent W. Zheng, Han Yu, and Chunyan Miao. 2019. A survey of zero-shot learning: Settings, methods, and applications. TIST, 10(2):1:37. [find](https://scholar.google.com/scholar?q=Wei+Wang%2C+Vincent+W%2E+Zheng%2C+Han+Yu%2C+and+Chunyan+Miao%2E+2019%2E+A+survey+of+zero%2Dshot+learning%3A+Settings%2C+methods%2C+and+applications%2E+TIST%2C+10%282%29%3A1%3A37%2E)
35. Jason Wei and Kai Zou. 2019. [Eda: Easy data augmentation techniques for boosting performance on text classification tasks](https://md.research.elamur.ai/free/en/papers/wei-2019-eda-easy-data-augmentation-techniques/). In Proc. of EMNLP-IJCNLP.
36. Shanchan Wu and Yifan He. 2019. Enriching pre-trained language model with entity information for relation classification. In Proc. of CIKM. [find](https://scholar.google.com/scholar?q=Shanchan+Wu+and+Yifan+He%2E+2019%2E+Enriching+pre%2Dtrained+language+model+with+entity+information+for+relation+classification%2E+In+Proc%2E+of+CIKM%2E)
37. Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020. [Unsupervised data augmentation for consistency training](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/). In Proc. of NeurIPS.
38. Kun Xu, Siva Reddy, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016. [Question answering on freebase via relation extraction and textual evidence](https://md.research.elamur.ai/free/en/papers/xu-2016-question-answering-freebase-relation-extraction/). In Proc. of ACL.
39. Lu Xu, Yew Ken Chia, and Lidong Bing. 2021. [Learning span-level interactions for aspect sentiment triplet extraction](https://md.research.elamur.ai/free/en/papers/xu-2021-learning-span-level-interactions-aspect/). In Proc. of ACL.
40. Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022. [Zerogen: Efficient zero-shot learning via dataset generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/). CoRR, arXiv:2202.07922.
41. Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. [Calibrate before use: Improving few-shot performance of language models](https://md.research.elamur.ai/free/en/papers/zhao-2021-calibrate-before-use-improving-few/). In Proc. of ICML.
42. Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021. Meta-tuning language models to answer prompts better. In Findings of EMNLP. [find](https://scholar.google.com/scholar?q=Ruiqi+Zhong%2C+Kristy+Lee%2C+Zheng+Zhang%2C+and+Dan+Klein%2E+2021%2E+Meta%2Dtuning+language+models+to+answer+prompts+better%2E+In+Findings+of+EMNLP%2E)
43. Zexuan Zhong and Danqi Chen. 2021. [A frustratingly easy approach for entity and relation extraction](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/). In Proc. of NAACL. *Figure 7: Additional sentence samples from the datasets. The head and tail entities are shown in blue and orange, respectively.* *Figure 8: Additional synthetic samples from the g…

*Updated 24 October 2026*