/awesome-data-augmentation

awesome-data-augmentation

This repo is a collection of AWESOME things regarding data augmentation techniques in NLP, CV and GraphML, including papers, code, etc. Feel free to star and fork.

Data Augmentation in NLP

ACL 2020

  1. [SDA'20] Syntactic Data Augmentation Increases Robustness to Inference Heuristics. ACL 2020.

    Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen.

  2. [PDA'20] Parallel Data Augmentation for Formality Style Transfer. ACL 2020.

    Yi Zhang, Tao Ge, Xu Sun.

  3. [LGDA'20] Logic-Guided Data Augmentation and Regularization for Consistent Question Answering. ACL 2020.

    Akari Asai, Hannaneh Hajishirzi.

  4. [GEC'20] Good-Enough Compositional Data Augmentation. ACL 2020.

    Jacob Andreas.

  5. [EU'20] Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity. ACL 2020.

    Yuxia Wang, Fei Liu, Karin Verspoor, Timothy Baldwin.

  6. [TRB'20] Towards Reversal-Based Textual Data Augmentation for NLI Problems with Opposable Classes. ACL 2020.

    Alexey Tarasov.

  7. [HTYD'20] How to Tame Your Data: Data Augmentation for Dialog State Tracking. ACL 2020.

    Adam Summerville, Jordan Hashemi, James Ryan, William Ferguson.

  8. [DATD'20] Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors. ACL 2020.

    Longshaokan Wang, Maryam Fazel-Zarandi, Aditya Tiwari, Spyros Matsoukas, Lazaros Polymenakos.

  9. [DATB'20] Data Augmentation for Transformer-based G2P. ACL 2020.

    Zach Ryan, Mans Hulden.

EMNLP 2020

  1. [LAB'20] Local Additivity Based Data Augmentation for Semi-supervised NER. EMNLP 2020.

    Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang.

  2. [SSMBA'20] SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness. EMNLP 2020.

    Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi.

  3. [LP'20] Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization. EMNLP 2020.

    Zhenjie Zhao, Evangelos Papalexakis, Xiaojuan Ma.

  4. [VHDA'20] Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation. EMNLP 2020.

    Kang Min Yoo, Hanbit Lee, Franck Dernoncourt, Trung Bui, Walter Chang, Sang-goo Lee.

  5. [SDA'20] Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging. EMNLP 2020.

    Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, Nitish Shirish Keskar, Richard Socher, Caiming Xiong.

  6. [CMR'20] Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies. EMNLP 2020.

    Chris Kedzie, Kathleen McKeown.

  7. [SL'20] Sequence-Level Mixed Sample Data Augmentation. EMNLP 2020.

    Demi Guo, Yoon Kim, Alexander Rush.

  8. [TMH'20] Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space. EMNLP 2020.

    Dayiheng Liu, Yeyun Gong, Jie Fu, Yu Yan, Jiusheng Chen, Jiancheng Lv, Nan Duan, Ming Zhou.

  9. [DAGA'20] DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks. EMNLP 2020.

    Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao.

  10. [TDA'20] Textual Data Augmentation for Efficient Active Learning on Tiny Datasets. EMNLP 2020.

    Husam Quteineh, Spyridon Samothrakis, Richard Sutcliffe.

  11. [DB'20] Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation. EMNLP 2020.

    Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, Soroush Vosoughi.

  12. [TextAttack'20] TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP. EMNLP 2020.

    John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, Yanjun Qi.

  13. [PHICON'20] PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation. EMNLP 2020.

    Xiang Yue, Shuang Zhou.

  14. [GenAug'20] GenAug: Data Augmentation for Finetuning Text Generators.EMNLP 2020.

    Steven Y. Feng, Varun Gangal, Dongyeop Kang, Teruko Mitamura, Eduard Hovy.

  15. [GDA'20] Generative Data Augmentation for Commonsense Reasoning. EMNLP 2020.

    Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey.

  16. [HE'20] How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?. EMNLP 2020.

    Shayne Longpre, Yu Wang, Chris DuBois.

  17. [TDA'20] Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures. EMNLP 2020.

    Lin Miao, Mark Last, Marina Litvak.

  18. [NS'20] Noising Scheme for Data Augmentation in Automatic Post-Editing. EMNLP 2020.

    WonKee Lee, Jaehun Shin, Baikjin Jung, Jihyung Lee, Jong-Hyeok Lee.

  19. [QE'20] Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation. EMNLP 2020.

    Omid Kashefi, Rebecca Hwa.

  20. [CIT'20] Linguist Geeks on WNUT-2020 Task 2: COVID-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation. EMNLP 2020.

    Vasudev Awatramani, Anupam Kumar.

  21. [DA'20] NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative. EMNLP 2020.

    Kumud Chauhan.

EACL 2021

  1. [DAFH'21] Data Augmentation for Hypernymy Detection. EACL 2021.

    Thomas Kober, Julie Weeds, Lorenzo Bertolini, David Weir.

  2. [FSL'21] Few-shot learning through contextual data augmentation. EACL 2021.

    Farid Arthaud, Rachel Bawden, Alexandra Birch.

  3. [DAVA'21] Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase. EACL 2021.

    Akhila Yerukola, Mason Bretan, Hongxia Jin.

  4. [SSD'21] Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation. EACL 2021.

    Abeer Abuzayed, Hend Al-Khalifa.

NAACL 2021

  1. [CDA'21] Counterfactual Data Augmentation for Neural Machine Translation. NAACL 2021.

Qi Liu, Matt Kusner, Phil Blunsom.

  1. [AS'21] Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks. NAACL 2021.

Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych.

  1. [IZ'21] Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation. NAACL 2021.

    Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad.

  2. [TA'21] Target-Aware Data Augmentation for Stance Detection. NAACL 2021.

Yingjie Li, Cornelia Caragea.

  1. [FS'21] Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning. NAACL 2021.

    Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng, Shiqi Xu.

  2. [TDA'21] Training Data Augmentation for Code-Mixed Translation. NAACL 2021.

    Abhirut Gupta, Aditya Vavre, Sunita Sarawagi.

  3. [SC'21] Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation. NAACL 2021.

    Seiichiro Kondo, Kengo Hotate, Tosho Hirasawa, Masahiro Kaneko, Mamoru Komachi.

  4. [TopGuNN'21] TopGuNN: Fast NLP Training Data Augmentation using Large Corpora. NAACL 2021.

    Rebecca Iglesias-Flores, Megha Mishra, Ajay Patel, Akanksha Malhotra, Reno Kriz, Martha Palmer, Chris Callison-Burch.

  5. [DAC'21] UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation. NAACL 2021.

Frances Adriana Laureano De Leon, Harish Tayyar Madabushi, Mark Lee.

  1. [CG'21] Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management. NAACL 2021.

Milan Gritta, Gerasimos Lampouras, Ignacio Iacobacci.

ACL 2021

  1. [AugNLG'21] AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation. ACL 2021.

    Xinnuo Xu, Guoyin Wang, Young-Bum Kim, Sungjin Lee.

  2. [CVRM'21] Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability. ACL 2021.

Venelin Kovatchev, Phillip Smith, Mark Lee, Rory Devine.

  1. [UXLA'21] UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP. ACL 2021.

    M Saiful Bari, Tasnim Mohiuddin, Shafiq Joty.

  2. [DATG'21] Data Augmentation for Text Generation Without Any Augmented Data. ACL 2021.

    Wei Bi, Huayang Li, Jiacheng Huang.

  3. [LearnDA'21] LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification. ACL 2021.

    Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen.

  4. [CLSS'21] Cross-language Sentence Selection via Data Augmentation and Rationale Training. ACL 2021.

    Yanda Chen, Chris Kedzie, Suraj Nair, Petra Galuscakova, Rui Zhang, Douglas Oard, Kathleen McKeown.

  5. [HiddenCut'21] HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability. ACL 2021.

    Jiaao Chen, Dinghan Shen, Weizhu Chen, Diyi Yang.

  6. [CoRI'21] CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction. ACL 2021.

    Zhengbao Jiang, Jialong Han, Bunyamin Sisman, Xin Luna Dong.

  7. [DAAT'21] Data Augmentation with Adversarial Training for Cross-Lingual NLI. ACL 2021.

    Xin Dong, Yaxin Zhu, Zuohui Fu, Dongkuan Xu, Gerard de Melo.

  8. [MulDA'21] MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER. ACL 2021.

    Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao.

  9. [NRQA'21] Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation. ACL 2021.

    Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer.

  10. [AO'21] Avoiding Overlap in Data Augmentation for AMR-to-Text Generation. ACL 2021.

Wenchao Du, Jeffrey Flanigan.

  1. [DAUM'21] Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings. ACL 2021.

Sosuke Nishikawa, Ryokan Ri, Yoshimasa Tsuruoka.

  1. [CAiRE'21] CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System. ACL 2021.

Yan Xu, Etsuko Ishii, Genta Indra Winata, Zhaojiang Lin, Andrea Madotto, Zihan Liu, Peng Xu, Pascale Fung.

  1. [ASDA'21] A Survey of Data Augmentation Approaches for NLP. ACL 2021.

Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy.

  1. [BRMC'21] Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning. ACL 2021.

Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun.

  1. [SS'21] Substructure Substitution: Structured Data Augmentation for NLP. ACL 2021.

Haoyue Shi, Karen Livescu, Kevin Gimpel.

  1. [NN'21] Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax. ACL 2021.

Ehsan Kamalloo, Mehdi Rezagholizadeh, Peyman Passban, Ali Ghodsi.

  1. [mixSeq'21] mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation. ACL 2021.

Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Yang Fan, Tao Qin.

  1. [DABC'21] Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution. ACL 2021.

Toan Q. Nguyen, Kenton Murray, David Chiang.

  1. [TUA'21] The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation. ACL 2021.

Xin Su, Yiyun Zhao, Steven Bethard.

  1. [NWM'21] Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation. ACL 2021.

Zheng Yuan, David Strohmaier.

  1. [RMDA'21] NLPIITR at SemEval-2021 Task 6: RoBERTa Model with Data Augmentation for Persuasion Techniques Detection. ACL 2021.

Vansh Gupta, Raksha Sharma.

  1. [DPT'21] LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation. ACL 2021.

Dia Abujaber, Ahmed Qarqaz, Malak A. Abdullah.

  1. [DAL'21] Data augmentation for low-resource grapheme-to-phoneme mapping. ACL 2021.

Michael Hammond.

  1. [BSS'21] BME Submission for SIGMORPHON 2021 Shared Task 0. A Three Step Training Approach with Data Augmentation for Morphological Inflection. ACL 2021.

Gábor Szolnok, Botond Barta, Dorina Lakatos, Judit Ács.

  1. [ZPDA'21] Zero-pronoun Data Augmentation for Japanese-to-English Translation. ACL 2021.

    Ryokan Ri, Toshiaki Nakazawa, Yoshimasa Tsuruoka.

EMNLP 2021

  1. [RCDA'21] Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification. EMNLP 2021.

    Hao Chen, Rui Xia, Jianfei Yu.

  2. [GOLD'21] GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation. EMNLP 2021.

Derek Chen, Zhou Yu.

  1. [ECL'21] Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning. EMNLP 2021.

Seonghyeon Ye, Jiseon Kim, Alice Oh.

  1. [MRC'21] Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction. EMNLP 2021.

Jian Liu, Yufeng Chen, Jinan Xu.

  1. [VDA'21] Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models. EMNLP 2021.

    Kun Zhou, Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, Ji-Rong Wen.

  2. [SPDA'21] Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis. EMNLP 2021.

    Ting-Wei Hsu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen.

  3. [UDA'21] Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data. EMNLP 2021.

    David Lowell, Brian Howard, Zachary C. Lipton, Byron Wallace.

  4. [DACD'21] Data Augmentation for Cross-Domain Named Entity Recognition. EMNLP 2021.

    Shuguang Chen, Gustavo Aguilar, Leonardo Neves, Thamar Solorio.

  5. [SCDA'21] Simple Conversational Data Augmentation for Semi-supervised Abstractive Dialogue Summarization. EMNLP 2021.

    Jiaao Chen, Diyi Yang.

  6. [SDA'21] Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering. EMNLP 2021.

    Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot, Djamé Seddah, Jacopo Staiano.

  7. [RDA'21] Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach. EMNLP 2021.

    Víctor M. Sánchez-Cartagena, Miquel Esplà-Gomis, Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez.

  8. [DAH'21] Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing. EMNLP 2021.

    Kun Wu, Lijie Wang, Zhenghua Li, Ao Zhang, Xinyan Xiao, Hua Wu, Min Zhang, Haifeng Wang.

  9. [HypMix'21] HypMix: Hyperbolic Interpolative Data Augmentation. EMNLP 2021.

    Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek.

  10. [DAIR'21] Data Augmentation of Incorporating Real Error Patterns and Linguistic Knowledge for Grammatical Error Correction. EMNLP 2021.

    Xia Li, Junyi He.

  11. [DAM'21] Data Augmentation Methods for Anaphoric Zero Pronouns. EMNLP 2021.

    Abdulrahman Aloraini, Massimo Poesio.

  12. [IDS'21] Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation. EMNLP 2021.

    Jarana Manotumruksa, Jeff Dalton, Edgar Meij, Emine Yilmaz.

  13. [AEDA'21] AEDA: An Easier Data Augmentation Technique for Text Classification. EMNLP 2021.

    Akbar Karimi, Leonardo Rossi, Andrea Prati.

  14. [LDA'21] Learning Data Augmentation Schedules for Natural Language Processing. EMNLP 2021.

Daphné Chopard, Matthias S. Treder, Irena Spasić.

  1. [SH'21] Sister Help: Data Augmentation for Frame-Semantic Role Labeling. EMNLP 2021.

    Ayush Pancholy, Miriam R L Petruck, Swabha Swayamdipta.

  2. [AuGPT'21] AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models. EMNLP 2021.

Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, Ondřej Dušek.

  1. [LRQA'21] Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation. EMNLP 2021.

Yang Deng, Wenxuan Zhang, Wai Lam.

ACL 2022

  1. [CipherDAug'22] CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation. ACL 2022.

    Nishant Kambhatla, Logan Born, Anoop Sarkar.

  2. [MELM'22] MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER. ACL 2022.

    Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao.

  3. [CFRL'22] Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation. ACL 2022.

    Chengwei Qin, Shafiq Joty.

  4. [PromDA'22] PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks. ACL 2022.

    Yufei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, Xiubo Geng, Daxin Jiang.

  5. [FlipDA'22] FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning. ACL 2022.

    Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, Zhilin Yang.

  6. [STR'22] Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation. ACL 2022.

    Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler.

  7. [TS'22] Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks. ACL 2022.

    Xing Wu, Chaochen Gao, Meng Lin, Liangjun Zang, Songlin Hu.

  8. [DABF'22] Data Augmentation for Biomedical Factoid Question Answering. ACL 2022.

    Dimitris Pappas, Prodromos Malakasiotis, Ion Androutsopoulos.

  9. [SBDA'22] Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts. ACL 2022.

    Uyen Phan, Nhung Nguyen.

  10. [DARS'22] Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection. ACL 2022.

    Bosung Kim, Ndapa Nakashole.

  11. [HZ'22] Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding. ACL 2022.

    Matúš Falis, Hang Dong, Alexandra Birch, Beatrice Alex.

  12. [TDA'22] DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil. ACL 2022.

    Vasanth Palanikumar, Sean Benhur, Adeep Hande, Bharathi Raja Chakravarthi.

  13. [EDA'22] BpHigh@TamilNLP-ACL2022: Effects of Data Augmentation on Indic-Transformer based classifier for Abusive Comments Detection in Tamil. ACL 2022.

    Bhavish Pahwa.

  14. [RDA'22] Retrieval Data Augmentation Informed by Downstream Question Answering Performance. ACL 2022.

    James Ferguson, Hannaneh Hajishirzi, Pradeep Dasigi, Tushar Khot.

  15. [AUS'22] When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation. ACL 2022.

    Ehsan Kamalloo, Mehdi Rezagholizadeh, Ali Ghodsi.

  16. [LDC'22] Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text. ACL 2022.

    Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan.

  17. [DA'22] Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue. ACL 2022.

    Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen.

  18. [CNEG'22] Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation. ACL 2022.

    Tianchi Yue, Shulin Liu, Huihui Cai, Tao Yang, Shengkang Song, TingHao Yu.

  19. [AMR'22] AMR-DA: Data Augmentation by Abstract Meaning Representation. ACL 2022.

    Ziyi Shou, Yuxin Jiang, Fangzhen Lin.

  20. [AR'22] Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation. ACL 2022.

    Kevin Yang, Olivia Deng, Charles Chen, Richard Shin, Subhro Roy, Benjamin Van Durme.

  21. [CL'22] Cross-lingual Inflection as a Data Augmentation Method for Parsing. ACL 2022.

    Alberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, David Vilares.

  22. [OIDA'22] On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing. ACL 2022.

    Itsuki Okimura, Machel Reid, Makoto Kawano, Yutaka Matsuo.

  23. [IMT'22] Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies. ACL 2022.

    Daniel Zhang, Jiang Yu, Pragati Verma, Ashwinkumar Ganesan, Sarah Campbell.

  24. [ESM'22] FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification. ACL 2022.

    Filip Nilsson, György Kovács.

  25. [DAIS'22] Data Augmentation for Intent Classification with Off-the-shelf Large Language Models. ACL 2022.

Gaurav Sahu, Pau Rodriguez, Issam Laradji, Parmida Atighehchian, David Vazquez, Dzmitry Bahdanau.

  1. [Clozer'22] Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension. ACL 2022.

Holy Lovenia, Bryan Wilie, Willy Chung, Zeng Min, Samuel Cahyawijaya, Dan Su, Pascale Fung.

Findings 2022

  1. [DALR'22] Data Augmentation for Low-Resource Dialogue Summarization. Findings 2022.

    Yongtai Liu, Joshua Maynez, Gonçalo Simões, Shashi Narayan.

  2. [TGD'22] Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation. Findings 2022.

    Prakhar Gupta, Harsh Jhamtani, Jeffrey Bigham.

  3. [EMG'22] Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation. Findings 2022.

    Yong Cao, Wei Li, Xianzhi Li, Min Chen, Guangyong Chen, Long Hu, Zhengdao Li, Kai Hwang.

NAACL 2022

  1. [DAD'22] Data Augmentation with Dual Training for Offensive Span Detection. NAACL 2022.

    Nasim Nouri.

  2. [PM'22] Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers. NAACL 2022.

    Vivek Kumar, Rishabh Maheshwary, Vikram Pudi.

  3. [GCD'22] Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction. NAACL 2022.

    Junjie Li, Jianfei Yu, Rui Xia.

  4. [ICG'22] Improving Compositional Generalization with Latent Structure and Data Augmentation. NAACL 2022.

    Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Nowak, Tal Linzen, Fei Sha, Kristina Toutanova.

  5. [EPiDA'22] EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification. NAACL 2022.

    Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou.

  6. [TreeMix'22] TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding. NAACL 2022.

    Le Zhang, Zichao Yang, Diyi Yang.

  7. [EP'22] ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization. NAACL 2022.

    Mengsay Loem, Sho Takase, Masahiro Kaneko, Naoaki Okazaki.

  8. [IC'22] Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation. NAACL 2022.

    Xiruo Ding, Kevin Lybarger, Justin Tauscher, Trevor Cohen.

  9. [GraDA'22] GraDA: Graph Generative Data Augmentation for Commonsense Reasoning. NAACL 2022.

    Adyasha Maharana, Mohit Bansal.

  10. [ZQA'22] ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System. NAACL 2022.

    Chia-Chien Hung, Tommaso Green, Robert Litschko, Tornike Tsereteli, Sotaro Takeshita, Marco Bombieri, Goran Glavaš, Simone Paolo Ponzetto.

  11. [IG'22] UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation. NAACL 2022.

    Injy Sarhan, Pablo Mosteiro, Marco Spruit.

  12. [PC'22] Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation. NAACL 2022.

    Sahil Bhatt, Manish Shrivastava.

  13. [TA'22] Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection. NAACL 2022.

    Alejandro Mosquera.

  14. [EDA'22] CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3. NAACL 2022.

    Daniel Saeedi, Sirwe Saeedi, Aliakbar Panahi, Alvis C.M. Fong.

  15. [SD'22] Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation. NAACL 2022.

    Mosab Shaheen, Shubham Nigam.

  16. [ACA'22] UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation. NAACL 2022.

    Amirhossein Abaskohi, Arash Rasouli, Tanin Zeraati, Behnam Bahrak.

  17. [ALI'22] HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity. NAACL 2022.

    Zihang Xu, Ziqing Yang, Yiming Cui, Zhigang Chen.

  18. [PT'22] ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity. NAACL 2022.

    Zhongan Chen, Weiwei Chen, YunLong Sun, Hongqing Xu, Shuzhe Zhou, Bohan Chen, Chengjie Sun, Yuanchao Liu.

  19. [IDA'22] MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis. NAACL 2022.

    Cong Chen, Jiansong Chen, Cao Liu, Fan Yang, Guanglu Wan, Jinxiong Xia.

  20. [AS'22] Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF. NAACL 2022.

    Jianglong He, Akshay Uppal, Mamatha N, Shiv Vignesh, Deepak Kumar, Aditya Kumar Sarda.

  21. [OL'22] TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla. NAACL 2022.

    Nazia Tasnim, Md. Istiak Shihab, Asif Shahriyar Sushmit, Steven Bethard, Farig Sadeque.

  22. [DAE'22] UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition. NAACL 2022.

    Hyunju Song, Steven Bethard.

  23. [DA'22] Sharing Data by Language Family: Data Augmentation for Romance Language Morpheme Segmentation. NAACL 2022.

    Lauren Levine.

  24. [CG'22] Compositional Generalization for Kinship Prediction through Data Augmentation. NAACL 2022.

    Kangda Wei, Sayan Ghosh, Shashank Srivastava.

Data Augmentataion in CV

Data Augmentataion in Graph