In this work, we present a text generation approach with multi-attribute control for data augmentation. We introduce CGA, a Variational Autoencoder architecture, to control, generate, and augment text. CGA is able to generate natural sentences with multiple controlled attributes by combining adversarial learning with a context-aware loss . The scalability of our approach is established through a single discriminator, independently of the number of attributes.  As the main application of our work, we test the potential of this new  model in a data augmentation use case . In a downstream NLP task, the sentences generated by our CGA model not only show significant improvements over a strong baseline, but also a classification performance very similar to real data. Furthermore, we are able to show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments .
 We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. CGA is able to generate natural English sentences controlling multiple semantic and syntactic attributes by combining adversarial learning with a context-aware loss and a cyclical word dropout routine. We demonstrate the value of the individual model components in an ablation study. The scalability of our approach is ensured through a single discriminator, independently of the number of attributes. We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments. As the main application of our work, we test the potential of this new NLG model in a data augmentation scenario . In a downstream NLP task, the sentences generated by our CGA model show significant improvements over a strong baseline, and a classification performance often comparable to adding same amount of additional real data .
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In this work, we present a text generation approach with multi-attribute control for data augmentation.
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Variational Autoencoder
conditional VAE
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sentences with multiple controlled
English sentences controlling multiple semantic and syntactic
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and a cyclical word dropout routine. We demonstrate the value of the individual model components in an ablation study.
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established
ensured
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We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments.
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NLG
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use case
scenario
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not only
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but also
and
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very similar to real data. Furthermore, we are able to show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments
often comparable to adding same amount of additional real data