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Data augmentation is an effective solution to data scarcity in low-resource scenarios.
Xlda: Cross-lingual data augmentation for natural language inference and question answering
Jasdeep Singh, Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2019 · 1905
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A information retrieval based on question and answering and ner for unstructured information without using sql
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UXLA: A robust unsupervised data augmentation framework for zero-resource cross-lingual NLP
M Saiful Bari, Tasnim Mohiuddin, and Shafiq Joty. 2021 · 1992
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Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang. 2002 · 2002
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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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Soft gazetteers for low-resource named entity recognition
Shruti Rijhwani, Shuyan Zhou, Graham Neubig, and Jaime Carbonell. 2020 · 2005
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Unsupervised cross-lingual adaptation for sequence tagging and beyond
Xin Li, Lidong Bing, Wenxuan Zhang, Zheng Li, and Wai Lam. 2020b · 2010
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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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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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Cross-lingual named entity recognition via wikification
Chen-Tse Tsai, Stephen Mayhew, and Dan Roth. 2016 · 2016
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Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017 · 2017
Cited alongside, same era.
Low-resource named entity recognition with cross-lingual, character-level neural conditional random fields
Ryan Cotterell and Kevin Duh. 2017 · 2017
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Learning to paraphrase for question answering
Li Dong, Jonathan Mallinson, Siva Reddy, and Mirella Lapata. 2017 · 2017
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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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Improving low resource named entity recognition using cross-lingual knowledge transfer
Xiaocheng Feng, Xiachong Feng, Bing Qin, Zhangyin Feng, and Ting Liu. 2018 · 2018
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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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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Template-based question generation from retrieved sentences for improved unsupervised question answering
Alexander Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2020 · 2020
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Data augmentation using pre-trained transformer models
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020 · 2020
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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Fast and accurate reading comprehension by combining self-attention and convolution
Adams Wei Yu, David Dohan, Quoc Le, Thang Luong, Rui Zhao, and Kai Chen. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 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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Conditional bert contextual augmentation
Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, and Songlin Hu. 2019 · 2019
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Dual adversarial neural transfer for low-resource named entity recognition
Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, and Kenneth Kwok. 2019 · 2019
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A rigorous study on named entity recognition: Can fine-tuning pretrained model lead to the promised land?
Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, and Nicholas Jing Yuan. 2020 · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
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Cosda-ml: Multi-lingual code-switching data augmentation for zero-shot cross-lingual nlp
Libo Qin, Minheng Ni, Yue Zhang, and Wanxiang Che. 2020 · 2020
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Extracting time expressions and named entities with constituent-based tagging schemes
Xiaoshi Zhong, Erik Cambria, and Amir Hussain. 2020 · 2020
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MulDA: A multilingual data augmentation framework for low-resource cross-lingual NER
Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, and Chunyan Miao. 2021 · 2021
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Cross-lingual aspect-based sentiment analysis with aspect term code-switching
Wenxuan Zhang, Ruidan He, Haiyun Peng, Lidong Bing, and Wai Lam. 2021 · 2021
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Time Expression and Named Entity Recognition
Xiaoshi Zhong and Erik Cambria. 2021 · 2021
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