Insufficient data can also rock! Learning to converse using smaller data with augmentation

Li，Juntao1,2; Qiu，Lisong1,2; Tang，Bo3; Chen，Dongmin1; Zhao，Dongyan1,2; Yan，Rui1,2

2019

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

6698-6705

Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational autoencoder (CVAE) is employed as the generator to output more training data with diversified expressions. To improve the correlation of each augmented training pair, we design a discriminator with adversarial training to supervise the augmentation process. Moreover, we thoroughly investigate various data augmentation schemes for neural dialogue system with generative models, both GAN and CVAE. Experimental results on two open corpora, Weibo and Twitter, demonstrate the superiority of our proposed data augmentation model.

其他

英语

EI

20203509102082

Speech processing ; Neural network models ; Social networking (online)

Ergonomics and Human Factors Engineering:461.4 ; Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4 ; Speech:751.5

2-s2.0-85074920576

Scopus

1.Center for Data Science,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,China
2.Institute of Computer Science and Technology,Peking University,Beijing,China
3.Department of Computer Science and Engineering,Southern University of Science and Technology,

Li，Juntao,Qiu，Lisong,Tang，Bo,et al. Insufficient data can also rock! Learning to converse using smaller data with augmentation[C],2019:6698-6705.