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Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models.
Wordnet: a lexical database for english
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Erik F. Tjong Kim Sang and Jorn Veenstra. 1999 · 1999
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
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Towards robust linguistic analysis using OntoNotes
Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun. 2015 · 2015
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Data augmentation for low-resource neural machine translation
Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017 · 2017
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Sequence-to-sequence data augmentation for dialogue language understanding
Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018 · 2018
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Data augmentation via dependency tree morphing for low-resource languages
Gözde Gül Şahin and Mark Steedman. 2018 · 2018
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Pooled contextualized embeddings for named entity recognition
Alan Akbik, Tanja Bergmann, and Roland Vollgraf. 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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Soft contextual data augmentation for neural machine translation
Fei Gao, Jinhua Zhu, Lijun Wu, Yingce Xia, Tao Qin, Xueqi Cheng, Wengang Zhou, and Tie-Yan Liu. 2019 · 2019
Cited alongside, same era.
Cross-domain NER using cross-domain language modeling
An analysis of simple data augmentation for named entity recognition
Xiang Dai and Heike Adel. 2020 · 2020
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DAGA: Data augmentation with a generation approach for low-resource tagging tasks
Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, and Chunyan Miao. 2020 · 2020
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Rethinking generalization of neural models: A named entity recognition case study
Jinlan Fu, Pengfei Liu, and Qi Zhang. 2020 · 2020
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Syntactic data augmentation increases robustness to inference heuristics
Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, and Tal Linzen. 2020 · 2020
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Temporally-informed analysis of named entity recognition
Shruti Rijhwani and Daniel Preotiuc-Pietro. 2020 · 2020
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Educating text autoencoders: Latent representation guidance via denoising
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Chen Jia, Xiaobo Liang, and Yue Zhang. 2019 · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter. 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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Generalized data augmentation for low-resource translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019 · 2019
Cited alongside, same era.
Zero-resource cross-domain named entity recognition
Zihan Liu, Genta Indra Winata, and Pascale Fung. 2020a
Cited in the paper.
Crossner: Evaluating cross-domain named entity recognition
Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, and Pascale Fung. 2020b
Cited in the paper.
T. Shen, Jonas Mueller, R. Barzilay, and T. Jaakkola. 2020 · 2020
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Multi-domain named entity recognition with genre-aware and agnostic inference
Jing Wang, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2020 · 2020
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SeqMix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2020
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