Abstract
With the development of e-commerce, it is necessary to build an online shopping guidance system to help users to choose the products they desired. Task-oriented dialogue systems can be used as an online shopping guidance system in e-commerce websites. Current dialogue systems can only extract basic attributes which are the inherent attributes of products. These systems can not process users’ requests containing high level attributes which describe products’ functions and user experience. These requests, however, appear frequently in real scenarios. To solve this problem, we build PARROT, an adaptive online shopping guidance system. PARROT can extract both basic and high level attributes from dialogues and recommend suitable products to users. The novel features of PARROT are as follows: (1) We propose a new architecture of task-oriented dialogue systems which can extract both basic and high level products’ attributes (functional attributes and experience attributes). (2) We construct knowledge base to map from high level attributes to basic level attributes or products. (3) We build a task oriented dialogue system which can finish the task of shopping guidance in websites. We test PARROT in three main scenarios and these tests demonstrate that PARROT can successfully recommend suitable products to users by extracting both basic and high level attributes.
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References
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Kong, J., Cai, Y., Ren, D., Li, Z.: Deep multi-task learning with cross connected layer for slot filling. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019, Part II. LNCS (LNAI), vol. 11839, pp. 308–317. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32236-6_27
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, 28 June–1 July 2001 [3]. pp. 282–289
Peng, B., Li, X., Gao, J., Liu, J., Wong, K.: Deep dyna-q: integrating planning for task-completion dialogue policy learning. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, Volume 1: Long Papers [4]. pp. 2182–2192
Peng, B., Li, X., Li, L., Gao, J., Çelikyilmaz, A., Lee, S., Wong, K.: Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9–11 September 2017 [5]. pp. 2231–2240
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112. Curran Associates, Inc. (2014)
Xu, P., Hu, Q.: An end-to-end approach for handling unknown slot values in dialogue state tracking. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15–20 July 2018, Volume 1: Long Papers [7]. pp. 1448–1457
Acknowledgement
This work was supported by National Natural Science Foundation of China (No. 62076100), National Key Research and Development Program of China (Standard knowledge graph for epidemic prevention and production recovering intelligent service platform and its applications), the Fundamental Research Funds for the Central Universities, SCUT (No. D2201300, D2210010), the Science and Technology Programs of Guangzhou (201902010046), the Science and Technology Planning Project of Guangdong Province (No. 2020B0101100002). The project has been supported by the Hong Kong Research Grants Council under the general research fund scheme (project number: PolyU 11204919).
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Ren, D. et al. (2021). PARROT: An Adaptive Online Shopping Guidance System. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_31
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DOI: https://doi.org/10.1007/978-3-030-85899-5_31
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