Abstract
Recently, the response generation for dialogue systems has become a research hotspot both in the academic and business communities. Existing personalized response generation methods mainly stand on the chatbot’s perspective, and focus on improving the conversation consistency according to the chatbot’s traits. However, for building an emotionally intelligent and human-like chatbot, it is essential to consider the user’s profile, such as interests, hobbies, and life experiences, and generate the personalized response from the user-oriented perspective. In this paper, we introduce the user profile aware personalized dialogue generation task. For sparse profile users, we extend Model-Agnostic Meta-Learning (MAML) method to quickly adapt to new profiles by leveraging only a few dialogue samples. Extensive experiments are conducted on a real-world dataset, and the results have validated the superiority of the proposed model over strong baseline methods.
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Notes
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All annotators are fluent English speakers and are familiar with annotating rules.
References
Bengio, S., Bengio, Y., Cloutier, J., Gecsei, J.: On the optimization of a synaptic learning rule. In: Preprints Conference on Optimality in Artificial and Biological Neural Networks, vol. 2 (1992)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Dinan, E., et al.: The second conversational intelligence challenge (ConvAI2). In: Escalera, S., Herbrich, R. (eds.) The NeurIPS 2018 Competition. TSSCML, pp. 187–208. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29135-8_7
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Gu, J., Wang, Y., Chen, Y., Cho, K., Li, V.O.: Meta-learning for low-resource neural machine translation. arXiv preprint arXiv:1808.08437 (2018)
Hancock, B., Bordes, A., Mazare, P.E., Weston, J.: Learning from dialogue after deployment: feed yourself, chatbot! arXiv preprint arXiv:1901.05415 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, P.S., Wang, C., Singh, R., Yih, W., He, X.: Natural language to structured query generation via meta-learning. arXiv preprint arXiv:1803.02400 (2018)
Huisman, M., van Rijn, J.N., Plaat, A.: A survey of deep meta-learning. arXiv preprint arXiv:2010.03522 (2020)
Joshi, C.K., Mi, F., Faltings, B.: Personalization in goal-oriented dialog. arXiv preprint arXiv:1706.07503 (2017)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)
Li, J., Galley, M., Brockett, C., Spithourakis, G.P., Gao, J., Dolan, B.: A persona-based neural conversation model (2016)
Lian, R., Xie, M., Wang, F., Peng, J., Wu, H.: Learning to select knowledge for response generation in dialog systems. arXiv preprint arXiv:1902.04911 (2019)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81. Association for Computational Linguistics, Barcelona, July 2004. https://www.aclweb.org/anthology/W04-1013
Luo, L., Huang, W., Zeng, Q., Nie, Z., Sun, X.: Learning personalized end-to-end goal-oriented dialog. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6794–6801 (2019)
Madotto, A., Lin, Z., Wu, C.S., Fung, P.: Personalizing dialogue agents via meta-learning. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5454–5459 (2019)
Meng, C., Ren, P., Chen, Z., Monz, C., Ma, J., de Rijke, M.: RefNet: a reference-aware network for background based conversation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8496–8503 (2020)
Naik, D.K., Mammone, R.J.: Meta-neural networks that learn by learning. In: 1992 Proceedings of the IJCNN International Joint Conference on Neural Networks, vol. 1, pp. 437–442. IEEE (1992)
van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
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)
Qiu, Y., Li, H., Li, S., Jiang, Y., Hu, R., Yang, L.: Revisiting correlations between intrinsic and extrinsic evaluations of word embeddings. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds.) CCL/NLP-NABD -2018. LNCS (LNAI), vol. 11221, pp. 209–221. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01716-3_18
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)
Schmidhuber, J.: Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. Ph.D. thesis, Technische Universität München (1987)
See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1073–1083 (2017)
Shum, H.Y., He, X., Li, D.: From Eliza to Xiaoice: challenges and opportunities with social chatbots. Front. Inf. Technol. Electron. Eng. 19(1), 10–26 (2018)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)
Thrun, S.: Lifelong learning algorithms. In: Thrun, S., Pratt, L. (eds.) Learning to Learn. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5529-2_8
Thrun, S., Pratt, L.: Learning to Learn. Springer, Heidelberg (2012)
Vaswani, A., et al.: Attention is all you need. arXiv (2017)
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Yavuz, S., Rastogi, A., Chao, G.L., Hakkani-Tur, D.: DeepCopy: grounded response generation with hierarchical pointer networks. arXiv preprint arXiv:1908.10731 (2019)
Yu, M., et al.: Diverse few-shot text classification with multiple metrics. arXiv preprint arXiv:1805.07513 (2018)
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: i have a dog, do you have pets too? (2018)
Zhang, Y., Ren, P., de Rijke, M.: Improving background based conversation with context-aware knowledge pre-selection. arXiv preprint arXiv:1906.06685 (2019)
Zheng, Y., Chen, G., Huang, M., Liu, S., Zhu, X.: Personalized dialogue generation with diversified traits. arXiv preprint arXiv:1901.09672 (2019)
Acknowledgments
The work was supported by the National Natural Science Foundation of China (61872074, 61772122, 62172086), and the Fundamental Research Funds for the Central Universities (N180716010, N2116008).
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Dong, W., Feng, S., Wang, D., Zhang, Y. (2022). I Know You Better: User Profile Aware Personalized Dialogue Generation. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_15
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