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makcedward committed Nov 24, 2021
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Expand Up @@ -148,6 +148,7 @@ pip install librosa>=0.7.1 matplotlib
* [ReservedAug supports generating all combinations](https://github.com/makcedward/nlpaug/pull/251)
* [Rollback to use native HuggingFace API from Huggingface pipeline to solve slow performance issue](https://github.com/makcedward/nlpaug/issues/248)
* [Added description to explain the model of WordEmbsAug is custom class](https://github.com/makcedward/nlpaug/issues/249)
* [Change random behavior to increase more augmentation samples](https://github.com/makcedward/nlpaug/pull/228)

See [changelog](https://github.com/makcedward/nlpaug/blob/master/CHANGE.md) for more details.

Expand All @@ -174,6 +175,8 @@ This library uses data (e.g. capturing from internet), research (e.g. following
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This package is cited by many books, workshop and academic research papers (70+). Here are some of examples and you may visit [here](https://github.com/makcedward/nlpaug/blob/master/CITED.md)) to get the full list.

## Workshops cited nlpaug
* S. Vajjala. [NLP without a readymade labeled dataset](https://rpubs.com/vbsowmya/tmls2021) at [Toronto Machine Learning Summit, 2021](https://www.torontomachinelearning.com/). 2021

Expand All @@ -182,59 +185,11 @@ This library uses data (e.g. capturing from internet), research (e.g. following
* A. Bartoli and A. Fusiello. [Computer Vision–ECCV 2020 Workshops](https://books.google.com/books?hl=en&lr=lang_en&id=0rYREAAAQBAJ&oi=fnd&pg=PR7&dq=nlpaug&ots=88bPp5rhnY&sig=C2ue8Xxbu09l59nAMOcVxWYvvWM#v=onepage&q=nlpaug&f=false). 2020

## Research paper cited nlpaug
* M. Raghu and E. Schmidt. [A Survey of Deep Learning for Scientific Discovery](https://arxiv.org/pdf/2003.11755.pdf). 2020
* H. Guan, J. Li, H. Xu and M. Devarakonda. [Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction](https://arxiv.org/ftp/arxiv/papers/2004/2004.06216.pdf). 2020
* X. He, K. Zhao and X. Chu. [AutoML: A Survey of the State-of-the-Art](https://arxiv.org/pdf/1908.00709.pdf). 2020
* S. Illium, R. Muller, A. Sedlmeier and C. Linnhoff-Popien. [Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms](https://arxiv.org/pdf/2008.04590.pdf). 2020
* D. Niederhut. [A Python package for text data enrichment](https://www.theoj.org/joss-papers/joss.02136/10.21105.joss.02136.pdf). 2020
* P. Ryan, S. Takafuji, C. Yang, N. Wilson and C. McBride. [Using Self-Supervised Learning of Birdsong for Downstream Industrial Audio Classification](https://openreview.net/pdf?id=_P9LyJ5pMDb). 2020
* Z. Shao, J. Yang and S. Ren. [Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets](https://arxiv.org/pdf/2006.08914.pdf). 2020
* S. Qiu, B. Xu, J. Zhang, Y. Wang, X. Shen, G. D. Melo, C. Long and X. Li [EasyAug: An Automatic Textual Data Augmentation Platform for Classification Tasks](https://dl.acm.org/doi/10.1145/3366424.3383552). 2020
* D. Nguyen, Q. H. Nguyen, M. Dao, D. Dang-Nguyen, C. Gurrin and B. T. Nguyen. [Duplicate Identification Algorithms in SaaS Platforms](http://doras.dcu.ie/24667/1/3379174.3392319.pdf). 2020
* A. Ollagnier and H. Williams. [Text Augmentation Techniques for Clinical Case Classification](https://www.researchgate.net/profile/Ollagnier_Anais/publication/343949092_Text_Augmentation_Techniques_for_Clinical_Case_Classification/links/5f49602b458515a88b810e4a/Text-Augmentation-Techniques-for-Clinical-Case-Classification.pdf). 2020
* V. Atliha and D. Šešok. [Text Augmentation Using BERT for Image Captioning](https://www.mdpi.com/2076-3417/10/17/5978/pdf). 2020
* Y. Ma, X. Xu, and Y. Li. [LungRN+NL: An Improved Adventitious Lung Sound Classification Using non-local block ResNet Neural Network with Mixup Data Augmentation](https://www.researchgate.net/profile/Yi_Ma5/publication/343524153_LungRNNL_An_Improved_Adventitious_Lung_Sound_Classification_Using_non-local_block_ResNet_Neural_Network_with_Mixup_Data_Augmentation/links/5f2e6158458515b7290d454d/LungRN-NL-An-Improved-Adventitious-Lung-Sound-Classification-Using-non-local-block-ResNet-Neural-Network-with-Mixup-Data-Augmentation.pdf). 2020
* S. N. Zisad, M. Shahadat and K. Andersson. [Speech emotion recognition in neurological disorders using Convolutional Neural Network](http://www.diva-portal.org/smash/get/diva2:1456134/FULLTEXT01.pdf). 2020
* M. Bhange and N. Kasliwal. [HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection](https://arxiv.org/pdf/2008.09820.pdf). 2020
* T. Deruyttere, S. Vandenhende, D. Grujicic, Y. Liu, L. V. Gool, M. Blaschko, T. v and M. Moens. [Commands 4 Autonomous Vehicles (C4AV) Workshop Summary](https://arxiv.org/pdf/2009.08792.pdf). 2020
* A. Tamkin, M. Wu and N. Goodman. [Viewmaker Networks: Learning Views for Unsupervised Representation Learning](https://arxiv.org/pdf/2010.07432.pdf). 2020
* A. Spiegel, V. Cheong, J E. Kaplan and A. Sanchez. [MK-SQUIT: Synthesizing Questions using Iterative Template-Filling](https://arxiv.org/pdf/2011.02566.pdf). 2020
* C. Zuo, N. Acharya and R. Banerjee. [Querying Across Genres for Medical Claims in News](https://www.aclweb.org/anthology/2020.emnlp-main.139.pdf). 2020
* A. Sengupta. [DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks](https://www.aclweb.org/anthology/2020.wnut-1.51.pdf). 2020
* V. Awatramani and A. Kumar. [Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation](https://www.aclweb.org/anthology/2020.wnut-1.59.pdf). 2020
* S. Gerani1, R. Tissot, A Ying, J. Redmon, A. Rimando and R. Hun. [Reducing suicide contagion effect by detecting sentences from media reports with explicit methods of suicide](https://crcs.seas.harvard.edu/files/crcs/files/ai4sg-21_paper_39.pdf). 2020
* B. Velichkov, S. Gerginov, P. Panayotov, S. Vassileva, G. Velchev, I. Koyche and S. Boytcheva. [Automatic ICD-10 codes association to diagnosis: Bulgarian case](https://dl.acm.org/doi/fullHtml/10.1145/3429210.3429224). 2020
* T. Li, X. Chen, S. Zhang, Z. Dong and K. Keutzer. [Cross-Domain Sentiment Classification with In-Domain Contrastive Learning](https://arxiv.org/pdf/2012.02943.pdf). 2020
* J. Mizgajski, A. Szymczak, M. Morzy, Ł. Augustyniak, P. Szymański and P. Żelasko. [Return on Investment in Machine Learning: Crossing the Chasm between Academia and Busines]
* K. Goel, N. Rajani, J. Vig, S. Tan, J. Wu, S. Zheng, C. Xiong, M. Bansal and C. Ré. [Robustness Gym: Unifying the NLP Evaluation Landscape](https://arxiv.org/pdf/2102.01813.pdf). 2021
* M. Xu, F. Zhang, X. Cui and W. Zhang. [Speech Emotion Recognition with Multiscale Area Attention and Data Augmentationon](https://arxiv.org/pdf/2102.01813.pdf). 2021
* M. Ciolino, D. Noever and J. Kalin. [Multilingual Augmenter: The Model Chooses](https://arxiv.org/pdf/2102.09708.pdf). 2021
* F. D. Pereira, F. Pires, S. C. Fonseca, E. H. T. Oliveira, L. S. G. Carvalho, D. B. F. Oliveira and A. I. Cristea. [Towards a Human-AI Hybrid System for Categorising Programming Problems](https://dl.acm.org/doi/pdf/10.1145/3408877.3432422). 2021
* D. Zhang, F. Nan, X. Wei, D. Li, H. Zhu, K. McKeown, R. Nallapati, A. Arnold and B. Xiang. [Supporting Clustering with Contrastive Learning](https://arxiv.org/pdf/2103.12953.pdf). 2021
* L. Zhu and T. Gosakti. [Augmenting Harper Valley Bank: Robust Automatic Speech Recognition](https://www.researchgate.net/profile/Lauren-Zhu/publication/350340847_Augmenting_Harper_Valley_Bank_Robust_Automatic_Speech_Recognition/links/605a9ead92851cd8ce61b59c/Augmenting-Harper-Valley-Bank-Robust-Automatic-Speech-Recognition.pdf). 2021
* P. Ruas, V. D. T. Andrade and F. M. Couto. [Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification](https://aclanthology.org/2021.smm4h-1.21.pdf). 2021
* V. d Pimpalkhute, P. Nakhate and T. Diwan. [IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification of Health-Related Tweets](https://aclanthology.org/2021.smm4h-1.24.pdf). 2021
* A. F. Aji, H. A. Wibowo, M. N. Nityasya, R. E. Prasojo and T. N Fatyanosa. [BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter](https://aclanthology.org/2021.smm4h-1.9.pdf). 2021
* V. Kovatchev, P. Smith, M. Lee, and R Devin. [Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability](https://arxiv.org/pdf/2106.01635.pdf). 2021
* D. R. Beddiar, M. S. Jahan and M. Oussalah. [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://arxiv.org/pdf/2106.04681.pdf). 2021
* Y. Hirota, N. Garcia, M. Otani, C. Chu, Y. Nakashima, I.Taniguchi and T. Onoye. [A Picture May Be Worth a Hundred Words for Visual Question Answering](https://arxiv.org/pdf/2106.13445.pdf)
* Z. Hu and Z. Wang. [Mining Consumer Brand Relationship from Social Media Data: A Natural Language Processing Approach](https://link.springer.com/chapter/10.1007/978-3-030-78609-0_47)
* S. N. Zisad, E. Chowdhury, M. S. Hossain, R. U. Islam and K. Andersson. [An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty](https://www.mdpi.com/1999-4893/14/7/213/htm). 2021
* S. Ullrich and M. Geierhos. [Towards Constructing Multi-hop Reasoning Chains using Local Cohesion](https://www.unibw.de/code-events/05_ullrich.pdf). 2021
* N. Ashraf, S. Butt, G. Sidorov and A. Gelbukh. [CIC at CheckThat! 2021: Fake News detection Using Machine Learning And Data Augmentation](http://ceur-ws.org/Vol-2936/paper-34.pdf). 2021
* M. A. Zaidi, S. Indurthi, B. Lee, N. K. Lakumarapu and S. Kim. [Infusing Future Information into Monotonic Attention Through Language Models](https://arxiv.org/pdf/2109.03121.pdf). 2021
* A. Deng and E. Shrestha. [BERT-based Transfer Learning with Synonym Augmentation for Question Answering](https://evanshrestha.com/bert_syn_paper.pdf). 2021
* W. Simoncini. [Meme Generation](https://blog.ashita.nl/p/how-to-build-your-own-meme-generator/paper.pdf). 2021
* S. Cao and L. Wang. [CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://arxiv.org/pdf/2109.09209.pdf). 2021
* D. Kanojia, M. Fomicheva, T. Ranasinghe, F. Blain, C. Orasan and L. Specia. [Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation](https://arxiv.org/pdf/2109.10859.pdf). 2021
* J. Novikova. [Robustness and Sensitivity of BERT Models Predicting Alzheimer’s Disease from Text](https://arxiv.org/pdf/2109.11888.pdf). 2021
* P. Aggarwal, M. E. Liman, D. Gold, and T. Zesch. [VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes](https://aclanthology.org/2021.woah-1.22.pdf). 2021
* M. Jarosz, P. Nawrocki and B. ́Sniezynski. [Multi-Platform Intelligent System for Multimodal Human-Computer Interaction](http://www.cai.sk/ojs/index.php/cai/article/view/2021_1_83/1076). 2021
* T. Nguyen, and F. Pernkopf. [Lung Sound Classification Using Co-tuning and Stochastic Normalization](https://arxiv.org/pdf/2108.01991.pdf). 2021

## Project cited nlpaug
* D. Garcia-Olano and A. Jain. [Generating Counterfactual Explanations using Reinforcement Learning Methods for Tabular and Text data](http://www.diegoolano.com/files/RL_course_Fall_2019_Final_Project.pdf). 2019
* L. Yi. [Avengers: Achieving Superhuman Performance for Question Answering on SQuAD 2.0 Using Multiple Data Augmentations, Randomized Mini-Batch Training and Architecture Ensembling](https://pdfs.semanticscholar.org/ce36/6e8f69a26ea84a65fc2b37d7492f6c8993fe.pdf). 2020
* Google: M. Raghu and E. Schmidt. [A Survey of Deep Learning for Scientific Discovery](https://arxiv.org/pdf/2003.11755.pdf). 2020
* Sirius XM: E. Jing, K. Schneck, D. Egan and S. A. Waterman. [Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts](https://arxiv.org/pdf/2110.07096.pdf). 2021
* Salesforce Research: B. Newman, P. K. Choubey and N. Rajani. [P-adapters: Robustly Extracting Factual Information from Language Modesl with Diverse Prompts](https://arxiv.org/pdf/2110.07280.pdf). 2021
* Salesforce Research: L. Xue, M. Gao, Z. Chen, C. Xiong and R. Xu. [Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks](https://arxiv.org/pdf/2110.04413.pdf). 2021


## Contributions
<table>
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