Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation

Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, Ce Zhang


Abstract
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.
Anthology ID:
2020.findings-emnlp.33
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
351–366
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.33
DOI:
10.18653/v1/2020.findings-emnlp.33
Bibkey:
Cite (ACL):
Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, and Ce Zhang. 2020. Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 351–366, Online. Association for Computational Linguistics.
Cite (Informal):
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation (Russo et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.33.pdf
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