Text Augmentation

15 papers with code • 0 benchmarks • 0 datasets

You can read these blog posts to get an overview of the approaches.

Libraries

Use these libraries to find Text Augmentation models and implementations
3 papers
3,229
2 papers
241

Latest papers with no code

Code-Switching Text Augmentation for Multilingual Speech Processing

no code yet • 7 Jan 2022

The pervasiveness of intra-utterance Code-switching (CS) in spoken content has enforced ASR systems to handle mixed input.

To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP

no code yet • 18 Nov 2021

Although NLP has recently witnessed a load of textual augmentation techniques, the field still lacks a systematic performance analysis on a diverse set of languages and sequence tagging tasks.

Roles of Words: What Should (n’t) Be Augmented in Text Augmentation on Text Classification Tasks?

no code yet • ACL ARR November 2021

Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios.

UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning

no code yet • ACL ARR November 2021

A typical approach is to leverage unsupervised and semi-supervised learning to train a neural encoder to produce representations of utterances that are adequate for clustering then perform clustering on the representations to detect unseen clusters of intents.

Drug Re-positioning via Text Augmented Knowledge Graph Embeddings

no code yet • NeurIPS Workshop AI4Scien 2021

Drug re-positioning, modeled as a link prediction problem over medical knowledge graphs (KG), has great potential in finding new usage or targets for approved medicine with relatively low cost.

What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification

no code yet • 1 Sep 2021

Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios.

BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data

no code yet • SEMEVAL 2021

We observe that joint learning improves the F1 scores on the SemTabFacts and TabFact test sets by 3. 31{\%} and 0. 77{\%}, respectively.

Multilingual Augmenter: The Model Chooses

no code yet • 19 Feb 2021

Natural Language Processing (NLP) relies heavily on training data.

Neural Data-to-Text Generation with LM-based Text Augmentation

no code yet • EACL 2021

Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.

Text Augmentation in a Multi-Task View

no code yet • EACL 2021

Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training.