Abstract
Current personalized dialogue systems do not thoroughly model the context to capture richer information, and still tend to generate short, incoherent and boring responses. To tackle these problems, in this paper we propose a generative adversarial network model PersonaGAN for personalized dialogue generation. In addition to hierarchical modeling of context, we introduce a speaker-aware encoder in the generator to capture richer context information. Besides, we apply adversarial training to personalized dialogue generation task via using a transformer-based matching model as discriminator. The discriminator could give higher rewards for the responses which look like human written and lower rewards for machine generated responses. Such training strategy encourages the generator to generate responses which are grammatically fluent, informative and logically coherent with context. We evaluate the proposed model on a public available dataset and yield promising results on both automatic and human evaluation, which show that our model can generate more coherent and personalized responses while ensuring fluency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Here we convert a session data to several dialogue data. For example, for a session \(S=\left\{ X_{1}, X_{2}, \ldots , X_{m}\right\} \), we convert it to \(\left\{ X_{1}, X_{2}\right\} \), \(\left\{ X_{1}, X_{2}, X_{3}\right\} , \)..., \(\left\{ X_{1}, X_{2}, \ldots , X_{m}\right\} \). Eventually, we have 131,428 for training and 7,799 for testing.
- 2.
- 3.
Code available at: https://github.com/pancraslv/Persona-GAN.
- 4.
- 5.
All annotators are fluent English speakers and are familiar with annotating rules.
References
Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
Goyal, A., Lamb, A., Zhang, Y., Zhang, S., Courville, A.C., Bengio, Y.: Professor forcing: A new algorithm for training recurrent networks. In: NIPS, pp. 4601–4609 (2016)
Kusner, M.J., Hernández-Lobato, J.M.: GANS for sequences of discrete elements with the Gumbel-softmax distribution. CoRR abs/1611.04051 (2016)
Li, J., Galley, M., Brockett, C., Gao, J., Dolan, B.: A diversity-promoting objective function for neural conversation models. In: NAACL, pp. 110–119 (2016)
Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, W.B.: A persona-based neural conversation model. In: ACL, pp. 994–1003 (2016)
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., Jurafsky, D.: Adversarial learning for neural dialogue generation. In: EMNLP, pp. 2157–2169 (2017)
Lian, R., Xie, M., Wang, F., Peng, J., Wu, H.: Learning to select knowledge for response generation in dialog systems. In: IJCAI, pp. 5081–5087 (2019)
Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A.F., Cambria, E.: DialogueRNN: an attentive RNN for emotion detection in conversations. In: AAAI, pp. 6818–6825 (2019)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Qian, Q., Huang, M., Zhao, H., Xu, J., Zhu, X.: Assigning personality/profile to a chatting machine for coherent conversation generation. In: IJCAI, pp. 4279–4285 (2018)
Serban, I.V., Sordoni, A., Bengio, Y., Courville, A.C., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: AAAI, pp. 3776–3784 (2016)
Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: ACL, pp. 1577–1586 (2015)
Song, H., Zhang, W., Cui, Y., Wang, D., Liu, T.: Exploiting persona information for diverse generation of conversational responses. In: IJCAI, pp. 5190–5196 (2019)
Soria-Comas, J., Domingo-Ferrer, J.: Big data privacy: challenges to privacy principles and models. Data Sci. Eng. 1(1), 21–28 (2016). https://doi.org/10.1007/s41019-015-0001-x
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)
Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
Weizenbaum, J.: ELIZA - a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)
Williams, J.D., Young, S.J.: Partially observable Markov decision processes for spoken dialog systems. Comput. Speech Lang. 21(2), 393–422 (2007)
Xing, C., Wu, Y., Wu, W., Huang, Y., Zhou, M.: Hierarchical recurrent attention network for response generation. In: AAAI, pp. 5610–5617 (2018)
Xu, J., Ren, X., Lin, J., Sun, X.: Diversity-promoting GAN: a cross-entropy based generative adversarial network for diversified text generation. In: EMNLP, pp. 3940–3949 (2018)
Yavuz, S., Rastogi, A., Chao, G., Hakkani-Tür, D.: Deepcopy: grounded response generation with hierarchical pointer networks. CoRR abs/1908.10731 (2019)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGan: sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852–2858 (2017)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: i have a dog, do you have pets too? In: ACL, pp. 2204–2213 (2018)
Zheng, Y., Chen, G., Huang, M., Liu, S., Zhu, X.: Personalized dialogue generation with diversified traits. CoRR abs/1901.09672 (2019)
Zhu, Q., Cui, L., Zhang, W., Wei, F., Liu, T.: Retrieval-enhanced adversarial training for neural response generation. In: ACL, pp. 3763–3773 (2019)
Acknowledgement
The work was supported by the National Key R&D Program of China under grant 2018YFB1004700, National Natural Science Foundation of China (61872074, 61772122), and the CETC Joint fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lv, P., Feng, S., Wang, D., Zhang, Y., Yu, G. (2020). PersonaGAN: Personalized Response Generation via Generative Adversarial Networks. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-59410-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59409-1
Online ISBN: 978-3-030-59410-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)