Fetching the paper…
Reading the bibliography…
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
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 · 1901
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
Rie Kubota Ando and Tong Zhang. 2005 · 2005
Earlier work this paper cites.
Named entity recognition for question answering. In Proceedings of the Australasian language technology workshop 2006 . 51–58
Diego Mollá, Menno Van Zaanen, and Daniel Smith. 2006 · 2006
Earlier work this paper cites.
Scalable training of L 1 L_{1} -regularized log-linear models. In Proceedings of the 24th International Conference on Machine Learning . 33–40
Galen Andrew and Jianfeng Gao. 2007 · 2007
Earlier work this paper cites.
A survey of named entity recognition and classification
David Nadeau and Satoshi Sekine. 2007 · 2007
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Named entity recognition in query. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval . 267–274
Jiafeng Guo, Gu Xu, Xueqi Cheng, and Hang Li. 2009 · 2009
Earlier work this paper cites.
Towards robust linguistic analysis using ontonotes. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning . 143–152
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
Earlier work this paper cites.
Learning from 26 Languages: Program Management and Science in the Babel Program. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers . Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 1
Mary Harper. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Yara Parser: A Fast and Accurate Dependency Parser
Mohammad Sadegh Rasooli and Joel R. Tetreault. 2015 · 2015
Earlier work this paper cites.
Stance Detection with Bidirectional Conditional Encoding. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Austin, Texas, 876–885
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016 · 2016
Earlier work this paper cites.
Named entity recognition with bidirectional LSTM-CNNs
Jason PC Chiu and Eric Nichols. 2016 · 2016
Earlier work this paper cites.
Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Berlin, Germany, 1–11
James Goodman, Andreas Vlachos, and Jason Naradowsky. 2016 · 2016
Earlier work this paper cites.
Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
Earlier work this paper cites.
Matching Networks for One Shot Learning. In Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra. 2016 · 2016
Earlier work this paper cites.
Results of the WNUT2017 shared task on novel and emerging entity recognition. In Proceedings of the 3rd Workshop on Noisy User-generated Text . 140–147
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017 · 2017
Earlier work this paper cites.
On calibration of modern neural networks. In International conference on machine learning . PMLR, 1321–1330
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Semi-supervised sequence tagging with bidirectional language models
Matthew E Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 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.
The GUM corpus: Creating multilayer resources in the classroom
Amir Zeldes. 2017 · 2017
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. 2018 · 2018
Earlier work this paper cites.
Transforming Wikipedia into a large-scale fine-grained entity type corpus. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)
Abbas Ghaddar and Philippe Langlais. 2018 · 2018
Earlier work this paper cites.
Sequence-to-sequence data augmentation for dialogue language understanding
Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018 · 2018
Earlier work this paper cites.
Deep contextualized word representations. CoRR abs/1802.05365 (2018)
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 1802 · 2018
Earlier work this paper cites.
Plato: Pre-trained dialogue generation model with discrete latent variable
Siqi Bao, Huang He, Fan Wang, Hua Wu, and Haifeng Wang. 2019 · 2019
Earlier work this paper cites.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Cited alongside, same era.
Low-resource name tagging learned with weakly labeled data
Yixin Cao, Zikun Hu, Tat-seng Chua, Zhiyuan Liu, and Heng Ji. 2019 · 2019
Cited alongside, same era.
Few-shot classification in named entity recognition task. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing . 993–1000
Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019 · 2019
Cited alongside, same era.
Cross-domain NER using cross-domain language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . 2464–2474
Chen Jia, Xiaobo Liang, and Yue Zhang. 2019 · 2019
Cited alongside, same era.
Rethinking preventing class-collapsing in metric learning with margin-based losses. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 10316–10325
Elad Levi, Tete Xiao, Xiaolong Wang, and Trevor Darrell. 2021 · 2021
Later among the works it cites.
NER-BERT: a pre-trained model for low-resource entity tagging
Zihan Liu, Feijun Jiang, Yuxiang Hu, Chen Shi, and Pascale Fung. 2021a · 2021
Later among the works it cites.
Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2021 · 2021
Later among the works it cites.
Locate and label: A two-stage identifier for nested named entity recognition
Yongliang Shen, Xinyin Ma, Zeqi Tan, Shuai Zhang, Wen Wang, and Weiming Lu. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Vl-bert: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2019 · 2019
Cited alongside, same era.
Videobert: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 7464–7473
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
Dialogpt: Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2019 · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations. In International conference on machine learning . PMLR, 1597–1607
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
Cited alongside, same era.
Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
Cited alongside, same era.
Peiyi Wang, Runxin Xu, Tianyu Liu, Qingyu Zhou, Yunbo Cao, Baobao Chang, and Zhifang Sui. 2021 · 2021
Later among the works it cites.
Calibrate before use: Improving few-shot performance of language models. In International Conference on Machine Learning . PMLR, 12697–12706
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Later among the works it cites.
Qianglong Chen, Feng-Lin Li, Guohai Xu, Ming Yan, Ji Zhang, and Yin Zhang. 2022 · 2022
Later among the works it cites.
Guanting Dong, Daichi Guo, Liwen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, et al · 2022
Later among the works it cites.
A Robust Contrastive Alignment Method for Multi-Domain Text Classification. In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 7827–7831
Xuefeng Li, Hao Lei, Liwen Wang, Guanting Dong, Jinzheng Zhao, Jiachi Liu, Weiran Xu, and Chunyun Zhang. 2022 · 2022
Later among the works it cites.
Contrastive Demonstration Tuning for Pre-trained Language Models
Xiaozhuan Liang, Ningyu Zhang, Siyuan Cheng, Zhen Bi, Zhenru Zhang, Chuanqi Tan, Songfang Huang, Fei Huang, and Huajun Chen. 2022 · 2022
Later among the works it cites.
Label Semantics for Few Shot Named Entity Recognition
Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, and Dan Roth. 2022a · 2022
Later among the works it cites.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Tingting Ma, Huiqiang Jiang, Qianhui Wu, Tiejun Zhao, and Chin-Yew Lin. 2022b · 2022
Later among the works it cites.
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 2460–2465
Gao Qixiang, Guanting Dong, Yutao Mou, Liwen Wang, Chen Zeng, Daichi Guo, Mingyang Sun, and Weiran Xu. 2022 · 2022
Later among the works it cites.
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Jianing Wang, Chengyu Wang, Chuanqi Tan, Minghui Qiu, Songfang Huang, Jun Huang, and Ming Gao. 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.
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
Shuhui Wu, Yongliang Shen, Zeqi Tan, and Weiming Lu. 2022 · 2022
Later among the works it cites.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Weihao Zeng, Keqing He, Zechen Wang, Dayuan Fu, Guanting Dong, Ruotong Geng, Pei Wang, Jingang Wang, Chaobo Sun, Wei Wu, and Weiran Xu. 2022 · 2022
Later among the works it cites.
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition. In Findings of the Association for Computational Linguistics: EMNLP 2022 . Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 6345–6350
Gang Zhao, Guanting Dong, Yidong Shi, Haolong Yan, Weiran Xu, and Si Li. 2022 · 2022
Later among the works it cites.
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2251–2262
Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, and Chunyan Miao. 2022 · 2022
Later among the works it cites.
Boundary Smoothing for Named Entity Recognition
Enwei Zhu and Jinpeng Li. 2022 · 2022
Later among the works it cites.
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Named Entity Recognition. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 1–5
Guanting Dong, Zechen Wang, Liwen Wang, Daichi Guo, Dayuan Fu, Yuxiang Wu, Chen Zeng, Xuefeng Li, Tingfeng Hui, Keqing He, Xinyue Cui, Qixiang Gao, and Weiran Xu. 2023 · 2023
Closest in time.
Revisit Out-Of-Vocabulary Problem For Slot Filling: A Unified Contrastive Framework With Multi-Level Data Augmentations. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 1–5
Daichi Guo, Guanting Dong, Dayuan Fu, Yuxiang Wu, Chen Zeng, Tingfeng Hui, Liwen Wang, Xuefeng Li, Zechen Wang, Keqing He, Xinyue Cui, and Weiran Xu. 2023 · 2023
Closest in time.
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting. In Findings of the Association for Computational Linguistics: ACL 2023 . Association for Computational Linguistics, Toronto, Canada, 825–834
Xuefeng Li, Liwen Wang, Guanting Dong, Keqing He, Jinzheng Zhao, Hao Lei, Jiachi Liu, and Weiran Xu. 2023 · 2023
Closest in time.
Improving Few-Shot Performance of DST Model Through Multitask to Better Serve Language-Impaired People. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) . 1–5
Mingyang Sun, Qixiang Gao, Yutao Mou, Guanting Dong, Ruifang Liu, and Wenbin Guo. 2023 · 2023
Closest in time.
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Chuanqi Tan, and Chang Zhou. 2023 · 2023
Closest in time.
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task. In Findings of the Association for Computational Linguistics: ACL 2023 . Association for Computational Linguistics, Toronto, Canada, 13139–13151
Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, and Hongzhi Zhang. 2023 · 2023
Closest in time.