Related papers: Data Augmentation for Neural NLP

Data Augmentation for Neural NLP

URL: http://arxiv.org/abs/2302.11412v1
Date: Wed, 22 Feb 2023 14:47:15 GMT
Title: Data Augmentation for Neural NLP
Authors: Domagoj Plu\v{s}\v{c}ec, Jan \v{S}najder
Abstract summary: Data augmentation is a low-cost approach for tackling data scarcity. This paper gives an overview of current state-of-the-art data augmentation methods used for natural language processing.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models. Such models are often deep learning models that require a significant amount of data to train. Acquiring data for various machine learning problems is accompanied by high labeling costs. Data augmentation is a low-cost approach for tackling data scarcity. This paper gives an overview of current state-of-the-art data augmentation methods used for natural language processing, with an emphasis on methods for neural and transformer-based models. Furthermore, it discusses the practical challenges of data augmentation, possible mitigations, and directions for future research.

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