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Machine Learning
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Application Areas
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Data Augmentation
3,622 papers
Papers per year
2002: 2
2006: 1
2008: 2
2009: 1
2011: 3
2012: 3
2013: 9
2014: 8
2015: 7
2016: 35
2017: 45
2018: 108
2019: 239
2020: 329
2021: 477
2022: 518
2023: 607
2024: 561
2025: 546
2026: 121
Papers
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
EMNLP 2022
Robustness of Demonstration-based Learning Under Limited Data Scenario
EMNLP 2022
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
EMNLP 2022
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation
EMNLP 2022
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval
EMNLP 2022
Conditional set generation using Seq2seq models
EMNLP 2022
Exploring Representation-level Augmentation for Code Search
EMNLP 2022
Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding
EMNLP 2022
CTRLsum: Towards Generic Controllable Text Summarization
EMNLP 2022
Simple Questions Generate Named Entity Recognition Datasets
EMNLP 2022
Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection
EMNLP 2022
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation
EMNLP 2022
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
EMNLP 2022
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation
EMNLP 2022
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems
EMNLP 2022
Detecting Label Errors by Using Pre-Trained Language Models
EMNLP 2022
Perturbation Augmentation for Fairer NLP
EMNLP 2022
Leveraging QA Datasets to Improve Generative Data Augmentation
EMNLP 2022
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling
EMNLP 2022
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
EMNLP 2022
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers
EMNLP 2022
Learning to Generate Overlap Summaries through Noisy Synthetic Data
EMNLP 2022
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering
EMNLP 2022
POTATO: The Portable Text Annotation Tool
EMNLP 2022
Is it out yet? Automatic Future Product Releases Extraction from Web Data
EMNLP 2022
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