NEO-NDA: Neo Natural Language Data Augmentation

Data augmentation generates synthetic data by making changes in data already obtained. It is applied to distinct data types like images, audio, and text. For textual data augmentation, many works propose restrictive transformations, for instance, they only work with one language (monolingual) or cre...

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Vydané v:2022 IEEE 16th International Conference on Semantic Computing (ICSC) s. 99 - 102
Hlavní autori: Ladeira, Lucas Z., Santos, Frances, Cleopas, Lucas, Buteneers, Pieter, Villas, Leandro
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Jazyk:English
Vydavateľské údaje: IEEE 01.01.2022
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data augmentation
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text classification
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Abstract Data augmentation generates synthetic data by making changes in data already obtained. It is applied to distinct data types like images, audio, and text. For textual data augmentation, many works propose restrictive transformations, for instance, they only work with one language (monolingual) or create samples with a fixed length [1]-[4]. In this work, we propose NEO Natural language Data Augmentation (NEO-NDA), a more comprehensive tool able to address data generation and rebalancing datasets. It supports data augmentation of minority classes. NEO-NDA is able to work with multiple languages, besides implementing distinct transformations to create new samples. Our results show that NEO-NDA was able to boost the performance of ML models with all datasets evaluated and, in some cases, doubling the performance in comparison with original datasets when no data augmentation method is used.
AbstractList Data augmentation generates synthetic data by making changes in data already obtained. It is applied to distinct data types like images, audio, and text. For textual data augmentation, many works propose restrictive transformations, for instance, they only work with one language (monolingual) or create samples with a fixed length [1]-[4]. In this work, we propose NEO Natural language Data Augmentation (NEO-NDA), a more comprehensive tool able to address data generation and rebalancing datasets. It supports data augmentation of minority classes. NEO-NDA is able to work with multiple languages, besides implementing distinct transformations to create new samples. Our results show that NEO-NDA was able to boost the performance of ML models with all datasets evaluated and, in some cases, doubling the performance in comparison with original datasets when no data augmentation method is used.
Author Cleopas, Lucas
Santos, Frances
Villas, Leandro
Ladeira, Lucas Z.
Buteneers, Pieter
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  surname: Villas
  fullname: Villas, Leandro
  email: leandro.villas@ic.unicamp.br
  organization: Institute of Computing University of Campinas (Unicamp),São Paulo,Brazil
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SubjectTerms Conferences
data augmentation
Data models
hybrid-approach
multilingual
Natural languages
Semantics
Switches
text classification
Title NEO-NDA: Neo Natural Language Data Augmentation
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