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arXiv 1910.034872019-10-04cs.CLcs.LGstat.ML

Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

Amazon Alexa AI（亚马逊Alexa AI）

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

备注：: EMNLP WNGT workshop

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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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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