Abstract
Dialogue generation is one of the most important parts in the dialogue system. Generating useful and informative responses in conversation has become a research hotspot. Previous work has proved that incorporating external knowledge is conducive to generating meaningful responses. But how to make full use of the existing information to select the most appropriate knowledge is a current research difficulty. In this paper, we propose a dialogue generation model with a lightweight knowledge routing module to sample knowledge needed for the conversation. In the knowledge routing module, we not only use the interactive information between the knowledge and the dialogue context utterance, but also the interactive information between each piece of knowledge for knowledge selection. Subsequently, the selected knowledge is incorporated into the dialogue generation model to generate responses. The experimental results show that our model tends to generate more meaningful and informative responses compared with baseline models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cho, K., van Merrienboer, B., Gulcehre, C., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP 2014, pp. 1724–1734 (2014)
Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: ACL, vol. 1, pp. 1577–1586 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS 2014, pp. 3104–3112 (2014)
Long, Y., Wang, J., Xu, Z., et al.: A knowledge enhanced generative conversational service agent. In: Proceedings of the 6th Dialog System Technology Challenges (DSTC6) Workshop (2017)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., et al.: Personalizing dialogue agents: i have a dog, do you have pets too? In: ACL, vol. 1, pp. 2204–2213 (2018)
Zheng, Y., Zhang, R., Huang, M., et al.: A pre-training based personalized dialogue generation model with persona-sparse data. In: AAAI 2020, pp. 9693–9700 (2020)
Zhou, H., Young, T., Huang, M., et al.: Commonsense knowledge aware conversation generation with graph attention. In: IJCAI 2018, pp. 4623–4629 (2018)
Dinan, E., Roller, S., Shuster, K., et al.: Wizard of Wikipedia: knowledge-powered conversational agents. In: ICLR (Poster) (2019)
Lian, R., Xie, M., Wang, F., et al.: Learning to select knowledge for response generation in dialog systems. In: IJCAI 2019, pp. 5081–5087 (2019)
Ghazvininejad, M., Brockett, C., Chang, M.-W., et al.: A knowledge-grounded neural conversation model. In: AAAI 2018, pp. 5110–5117 (2018)
Zhu, W., Mo, K., Zhang, Y., et al.: Flexible end-to-end dialogue system for knowledge grounded conversation. CoRR abs/1709.04264 (2017).
Gu, J., Lu, Z., Li, H., et al.: Incorporating copying mechanism in sequence-to-sequence learning. In: ACL, vol. 1 (2016)
Xu, Z., Liu, B., Wang, B., et al.: Incorporating loose-structured knowledge into conversation modeling via recall-gate LSTM. In: IJCNN 2017, pp. 3506–3513 (2017)
Cho, K., van Merrienboer, B., Gülçehre, Ç., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP 2014, pp. 1724–1734 (2014)
Yang, J., Yu, K., Gong, Y., et al.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR 2009, pp. 1794–1801 (2009)
Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013). https://doi.org/10.1007/s11263-013-0636-x
Shen, L., Sun, G., Huang, Q., et al.: Multi-level discriminative dictionary learning with application to large scale image classification. IEEE Trans. Image Process. 24(10), 3109–3123 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML 2010, pp. 807–814 (2010)
Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59497-3_175
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR 2015 (2015)
Zhao, T., Zhao, R., Eskénazi, M.: Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In: ACL, vol. 1, pp. 654–664 (2017)
Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: ACL 2002, pp. 311–318 (2002)
Li, J., Galley, M., Brockett, C., et al.: A diversity-promoting objective function for neural conversation models. In: HLT-NAACL 2016, pp. 110–119 (2016)
Acknowledgments
This research is supported by the National Natural Science Foundation of China (61532008, 61932008, 61572223), the Key Research and Development Program of Hubei Province (2020BAB017), Wuhan Science and Technology Program (2019010701011392), Scientific Research Center Program of National Language Commission (ZDI135-135) and the National Key Research and Development Program of China (2017YFC0909502).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, W., He, T., Zhang, M., Fan, R. (2021). Effectively Incorporating Knowledge in Open-Domain Dialogue Generation. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_19
Download citation
DOI: https://doi.org/10.1007/978-981-16-1964-9_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1963-2
Online ISBN: 978-981-16-1964-9
eBook Packages: Computer ScienceComputer Science (R0)