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Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
What Are People Asking About COVID-19? A Question Classification Dataset
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Context-based transformer models for answer sentence selection
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Training highly multiclass classifiers
Maya R. Gupta, Samy Bengio, and Jason Weston. 2014 · 2014
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin. 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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Cícero Nogueira dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. 2016 · 2016
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Deep metric learning via lifted structured feature embedding
H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese. 2016 · 2016
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Learning the curriculum with Bayesian optimization for task-specific word representation learning
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Robust training under linguistic adversity
Yitong Li, Trevor Cohn, and Timothy Baldwin. 2017 · 2017
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Taking into account inter-sentence similarity for update summarization
Maâli Mnasri, Gaël de Chalendar, and Olivier Ferret. 2017 · 2017
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Learning thematic similarity metric from article sections using triplet networks
Liat Ein Dor, Yosi Mass, Alon Halfon, Elad Venezian, Ilya Shnayderman, Ranit Aharonov, and Noam Slonim. 2018 · 2018
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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
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. 2019 · 2019
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Population based augmentation: Efficient learning of augmentation policy schedules
Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, and Pieter Abbeel. 2019 · 2019
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Improving answer selection and answer triggering using hard negatives
Sawan Kumar, Shweta Garg, Kartik Mehta, and Nikhil Rasiwasia. 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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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
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Rethinking curriculum learning with incremental labels and adaptive compensation
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Bruno Korbar, Du Tran, and Lorenzo Torresani. 2018 · 2018
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HuffPost news category dataset
Rishabh Misra. 2018 · 2018
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SwitchOut: an efficient data augmentation algorithm for neural machine translation
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018 · 2018
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Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir. 2018 · 2018
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Edinburgh neural machine translation systems for WMT 16
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b
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Madan Ravi Ganesh and Jason J. Corso. 2020 · 2020
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Easyaug: An automatic textual data augmentation platform for classification tasks
Siyuan Qiu, Binxia Xu, Jie Zhang, Yafang Wang, Xiaoyu Shen, Gerard de Melo, Chong Long, and Xiaolong Li. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le. 2020 · 2020
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Hierarchical text classification of Amazon product reviews
Kashnitsky Yury. 2020 · 2020
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