Fetching the paper…
Reading the bibliography…
Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data even if under zero-shot setting.
Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020 · 2004
Earlier work this paper cites.
Modeling relations and their mentions without labeled text
Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010 · 2010
Earlier work this paper cites.
Relation classification via convolutional deep neural network
Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014 · 2014
Earlier work this paper cites.
Classifying relations by ranking with convolutional neural networks
Cícero dos Santos, Bing Xiang, and Bowen Zhou. 2015 · 2015
Earlier work this paper cites.
Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom. 2016 · 2016
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Earlier work this paper cites.
Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Earlier work this paper cites.
Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D Manning. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Enriching pre-trained language model with entity information for relation classification
Shanchan Wu and Yifan He. 2019 · 2019
Earlier work this paper cites.
TACRED revisited: A thorough evaluation of the TACRED relation extraction task
Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 2020 · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
Cited alongside, same era.
Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
Cited alongside, same era.
Luke: Deep contextualized entity representations with entity-aware self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, and Yuji Matsumoto. 2020 · 2020
Cited alongside, same era.
ZS-BERT: Towards zero-shot relation extraction with attribute representation learning
Chih-Yao Chen and Cheng-Te Li. 2021 · 2021
Cited alongside, same era.
What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
Fastre: Towards fast relation extraction with convolutional encoder and improved cascade binary tagging framework
Guozheng Li, Xu Chen, Peng Wang, Jiafeng Xie, and Qiqing Luo. 2022 · 2022
Later among the works it cites.
Summarization as indirect supervision for relation extraction
Keming Lu, I Hsu, Wenxuan Zhou, Mingyu Derek Ma, Muhao Chen, et al. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Later among the works it cites.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2022 · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Label verbalization and entailment for effective zero-and few-shot relation extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021 · 2021
Cited alongside, same era.
Re-tacred: Addressing shortcomings of the tacred dataset
George Stoica, Emmanouil Antonios Platanios, and Barnabás Póczos. 2021 · 2021
Cited alongside, same era.
Gpt-j-6b: A 6 billion parameter autoregressive language model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
An improved baseline for sentence-level relation extraction
Wenxuan Zhou and Muhao Chen. 2021 · 2021
Cited alongside, same era.
Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022 · 2022
Cited alongside, same era.
Relationprompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria, and Luo Si. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Deepstruct: Pretraining of language models for structure prediction
Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, and Dawn Song. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Ask me anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel J Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré. 2023 · 2023
Closest in time.
Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. 2023 · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Closest in time.
Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun. 2023 · 2023
Closest in time.
Zero-shot information extraction via chatting with chatgpt
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, et al. 2023 · 2023
Closest in time.
Aligning instruction tasks unlocks large language models as zero-shot relation extractors
Kai Zhang, Bernal Jiménez Gutiérrez, and Yu Su. 2023 · 2023
Closest in time.
Re-matching: A fine-grained semantic matching method for zero-shot relation extraction
Jun Zhao, Wenyu Zhan, Xin Zhao, Qi Zhang, Tao Gui, Zhongyu Wei, Junzhe Wang, Minlong Peng, and Mingming Sun. 2023 · 2023
Closest in time.