Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

Nikolaos Malandrakis, Minmin Shen, Anuj Goyal, Shuyang Gao, Abhishek Sethi, Angeliki Metallinou


Abstract
Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoder-decoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5% absolute f-score in low-resource cases, validating the usefulness of our approach.
Anthology ID:
D19-5609
Volume:
Proceedings of the 3rd Workshop on Neural Generation and Translation
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | NGT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
90–98
Language:
URL:
https://www.aclweb.org/anthology/D19-5609
DOI:
10.18653/v1/D19-5609
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5609.pdf