natural-language-processing-8

Text Augmentation

3260 papers • 126 benchmarks • 313 datasets

You can read these blog posts to get an overview of the approaches. A Visual Survey of Data Augmentation in NLP

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in text-augmentation-24

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Libraries

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Use these libraries to find text-augmentation-24 models and implementations

makcedward/nlpaug

3 papers 4,291

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Most implemented papers

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Jason Wei, Kai Zou•Wed Jan 30 2019

EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion, which shows that EDA improves performance for both convolutional and recurrent neural networks.

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Content

dsfsi/textaugment

2 papers 370

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Paper Graph

Data Augmentation via Dependency Tree Morphing for Low-Resource Languages

Mark Steedman, Gözde Gül Şahin•Sun Aug 12 2018

It is shown that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.

130 0

Paper Graph

Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations

Sosuke Kobayashi•Mon Apr 30 2018

This work retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility and improves classifiers based on the convolutional or recurrent neural networks.

660 0

Paper Graph

PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

Yutong Xie, Qi Wu, Qi Chen, Sinuo Wang, Minh-Son To, Iris Lee, Ee Win Khoo, Kerolos Hendy, Daniel Koh, Yong Xia•Sat Apr 06 2024

A Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch that generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM).

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Paper Graph

Learning to Compose Domain-Specific Transformations for Data Augmentation

Alexander J. Ratner, Jared A. Dunnmon, Henry R. Ehrenberg, Zeshan Hussain, C. Ré•Tue Sep 05 2017

The proposed method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data, which can be used to perform data augmentation for any end discriminative model.

361 0

Paper Graph

Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding

Ting Liu, Yijia Liu, Wanxiang Che, Yutai Hou•Tue Jul 03 2018

A sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data to produce diverse utterances that help to improve the language understanding module.

158 0

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Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs

Claude Coulombe•Tue Dec 04 2018

This engineering work focuses on the use of practical, robust, scalable and easy-to-implement data augmentation pre-processing techniques similar to those that are successful in computer vision.

129 0

Paper Graph

Text Augmentation for Language Models in High Error Recognition Scenario

L. Burget, Karel Beneš•Tue Nov 10 2020

This work compares augmentation based on global error statistics with one based on per-word unigram statistics of ASR errors and concludes that it is better to only pay attention the global substitution, deletion and insertion rates.

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Improving short text classification through global augmentation methods

Vukosi Marivate, T. Sefara•Sat Jul 06 2019

The effect of different approaches to text augmentation is studied to provide insights for practitioners and researchers on making choices for augmentation for classification use cases and the use of \emph{mixup} further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model.

107 0

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Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Kiet Van Nguyen, N. Nguyen, Son T. Luu•Thu Sep 24 2020

The data augmentation techniques are applied to solve the imbalance problem between classes of the dataset, increasing the prediction model's accuracy.

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Paper Graph

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