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Active learning is an important technique for low-resource sequence labeling tasks.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019 · 1904
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Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019a · 1905
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Active 2 learning: Actively reducing redundancies in active learning methods for sequence tagging
Rishi Hazra, Shubham Gupta, and Ambedkar Dukkipati. 2019 · 1911
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Semi-supervised sequence modeling with cross-view training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning, and Quoc Le. 2018 · 1925
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Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky. 1992 · 1992
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Individual comparisons by ranking methods
Frank Wilcoxon. 1992 · 1992
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Committee-based sampling for training probabilistic classifiers
Ido Dagan and Sean P Engelson. 1995 · 1995
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Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
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Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel. 2001 · 2001
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew McCallum. 2005 · 2005
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Semantic role labeling as sequential tagging
Lluís Màrquez, Pere Comas, Jesús Giménez, and Neus Català. 2005 · 2005
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MMR-based active machine learning for bio named entity recognition
Seokhwan Kim, Yu Song, Kyungduk Kim, Jeong-Won Cha, and Gary Geunbae Lee. 2006 · 2006
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An approach to text corpus construction which cuts annotation costs and maintains reusability of annotated data
Katrin Tomanek, Joachim Wermter, and Udo Hahn. 2007 · 2007
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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Design challenges and misconceptions in named entity recognition
Lev Ratinov and Dan Roth. 2009 · 2009
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Deep learning for Chinese word segmentation and POS tagging
Xiaoqing Zheng, Hanyang Chen, and Tianyu Xu. 2013 · 2013
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Bidirectional lstm-crf models for sequence tagging
Zhiheng Huang, Wei Xu, and Kai Yu. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
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Neural architectures for named entity recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016 · 2016
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Generating text via adversarial training
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
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Paraphrase generation for semi-supervised learning in NLU
Eunah Cho, He Xie, and William M. Campbell. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Active entity recognition in low resource settings
Ning Gao, Nikos Karampatziakis, Rahul Potharaju, and Silviu Cucerzan. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Yizhe Zhang, Zhe Gan, and Lawrence Carin. 2016 · 2016
Cited alongside, same era.
Learning how to active learn: A deep reinforcement learning approach
Meng Fang, Yuan Li, and Trevor Cohn. 2017 · 2017
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
Cited alongside, same era.
Data augmentation for visual question answering
Kushal Kafle, Mohammed Yousefhussien, and Christopher Kanan. 2017 · 2017
Cited alongside, same era.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017 · 2017
Cited alongside, same era.
Data augmentation for morphological reinflection
Miikka Silfverberg, Adam Wiemerslage, Ling Liu, and Lingshuang Jack Mao. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Improved mixed-example data augmentation
Cecilia Summers and Michael J Dinneen. 2019 · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Exploring pre-trained language models for event extraction and generation
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019 · 2019
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How to invest my time: Lessons from human-in-the-loop entity extraction
Shanshan Zhang, Lihong He, Eduard Dragut, and Slobodan Vucetic. 2019 · 2019
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Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
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Bond: Bert-assisted open-domain named entity recognition with distant supervision
Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, and Chao Zhang. 2020 · 2020
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Named entity recognition without labelled data: A weak supervision approach
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin, and Samia Touileb. 2020 · 2020
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Denoising multi-source weak supervision for neural text classification
Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang. 2020 · 2020
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Steam: Self-supervised taxonomy expansion with mini-paths
Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, and Chao Zhang. 2020 · 2020
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