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Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity.
Language models are few-shot learners
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Data augmentation using pre-trained transformer models
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Fluent response generation for conversational question answering
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Automatic text summarization of covid-19 medical research articles using bert and gpt-2
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On data augmentation for extreme multi-label classification
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Topic-centric unsupervised multi-document summarization of scientific and news articles
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Semi-supervised classification for natural language processing
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Data augmentation for deep neural network acoustic modeling
Xiaodong Cui, Vaibhava Goel, and Brian Kingsbury. 2015 · 2015
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Audio augmentation for speech recognition
Tom Ko, Vijayaditya Peddinti, Daniel Povey, and Sanjeev Khudanpur. 2015 · 2015
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Going deeper with convolutions
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Short text classification based on feature extension using the n-gram model
Xinwei Zhang and Bin Wu. 2015 · 2015
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Improving neural machine translation models with monolingual data
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 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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Deceiving google’s perspective api built for detecting toxic comments
Data augmented relation extraction (dare) with gpt-2
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Language models are unsupervised multitask learners
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Making connections: A multi-disciplinary analysis of domestic homicide, mental health homicide and adult practice reviews
Amanda Lea Robinson, Alyson Rees, and Roxanna Dehaghani. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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Knowledge-based short text categorization using entity and category embedding
Rima Türker, Lei Zhang, Maria Koutraki, and Harald Sack. 2019 · 2019
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Classification for crisis-related tweets leveraging word embeddings and data augmentation
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Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Text classification based on fuzzy radial basis function
Zuhair Ali. 2019 · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019 · 2019
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Congcong Wang and David Lillis. 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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Exploring transformer text generation for medical dataset augmentation
Ali Amin-Nejad, Julia Ive, and Sumithra Velupillai. 2020 · 2020
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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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Go simple and pre-train on domain-specific corpora: On the role of training data for text classification
Aleksandra Edwards, Jose Camacho-Collados, Hélène de Ribaupierre, and Alun Preece. 2020 · 2020
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The devil is in the details: Evaluating limitations of transformer-based methods for granular tasks
Brihi Joshi, Neil Shah, Francesco Barbieri, and Leonardo Neves. 2020 · 2020
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020
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Modeling content importance for summarization with pre-trained language models
Liqiang Xiao, Lu Wang, Hao He, and Yaohui Jin. 2020 · 2020
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G-daug: Generative data augmentation for commonsense reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, and Doug Downey. 2020 · 2020
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Control prefixes for text generation
Jordan Clive, Kris Cao, and Marek Rei. 2021 · 2021
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Predicting themes within complex unstructured texts: A case study on safeguarding reports
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A sentence-level hierarchical bert model for document classification with limited labelled data
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