Abstract
With the development of the Internet, more and more people express their ideas on the internet in the form of short text. And a question with the same intention can be expressed in different ways. So it is necessary and important to understand the meaning of short text when we want to provide intelligent service to customer. Many studies have focused on the short texts based on public data sets. However, little studies have been carried out or can be effectively used in some specific fields. Taking logistics Intelligent Customer Service (ICS) as an example, the short texts has the above characteristics. To solve this issue about intention multiple-representation in logistics, a self-attention-based model, that is, One question to Many question (O2M) is proposed. On the basis of classification task, the model can learn the mapping relation from customer questions to standard questions. And it consists of three parts: standard questions domain, customer questions domain and selector. For the two domains, they learn semantic patterns of their own questions. And the selector becomes the bridge between them. Extensive experiments were carried out on logistics corpus. And the results show that the model is effective and the accuracy of the model is higher than that of traditional neural network models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August 2017, Volume 1: Long Papers, pp. 1657–1668 (2017). https://doi.org/10.18653/v1/P17-1152
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018). http://arxiv.org/abs/1810.04805
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580 (2012). http://arxiv.org/abs/1207.0580
Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Barzilay, R., Kan, M. (eds.) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August 2017, Volume 1: Long Papers, pp. 562–570. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/P17-1052
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Lapata, M., Blunsom, P., Koller, A. (eds.) Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, 3–7 April 2017, Volume 2: Short Papers, pp. 427–431. Association for Computational Linguistics (2017). https://doi.org/10.18653/v1/e17-2068
Kim, Y.: Convolutional neural networks for sentence classification. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746–1751. ACL (2014). https://doi.org/10.3115/v1/d14-1181
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: OpenNMT: open-source toolkit for neural machine translation. In: Proceedings of ACL (2017). https://doi.org/10.18653/v1/P17-4012
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Bonet, B., Koenig, S. (eds.) Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25–30 January 2015, Austin, Texas, USA, pp. 2267–2273. AAAI Press (2015). http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745
Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. In: Kambhampati, S. (ed.) Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2873–2879. IJCAI/AAAI Press (2016). http://www.ijcai.org/Abstract/16/408
Liu, Y., et al.: An enhanced ESIM model for sentence pair matching with self-attention. In: Proceedings of the Evaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2018), Tianjin, China, 14–17 August 2018, pp. 52–62 (2018). http://ceur-ws.org/Vol-224020..../paper09.pdf
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2–4 May 2013, Workshop Track Proceedings (2013). http://arxiv.org/abs/1301.3781
Sergio, G.C., Lee, M.: Stacked DeBERT: all attention in incomplete data for text classification. CoRR abs/2001.00137 (2020). http://arxiv.org/abs/2001.00137
Sun, J., et al.: “jieba” chinese text segmentation (2012). https://github.com/fxsjy/jieba
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4–9 December 2017, Long Beach, CA, USA, pp. 5998–6008 (2017). http://papers.nips.cc/paper/7181-attention-is-all-you-need
Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences. CoRR abs/1702.03814 (2017). http://arxiv.org/abs/1702.03814
Xie, J., et al.: Chinese text classification based on attention mechanism and feature-enhanced fusion neural network. Computing 102(3), 683–700 (2020). https://doi.org/10.1007/s00607-019-00766-9
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, Volume 2: Short Papers. The Association for Computer Linguistics (2016). https://doi.org/10.18653/v1/p16-2034
Acknowledgements
The authors would like to thank the funding from National Natural Science Foundation of China (Grant no. 61572305) and the resources and technical support from the High performance computing Center of Shanghai University, and Shanghai Engineering Research Center of Intelligent Computing System (No. 19DZ2252600). And it is especially grateful for the data and industry knowledge support from YTO express company.
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
Hu, J., Peng, J., Zhang, W., Qi, L., Hu, M., Zhang, H. (2020). Intention Multiple-Representation Model for Logistics Intelligent Customer Service. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12274. Springer, Cham. https://doi.org/10.1007/978-3-030-55130-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-55130-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55129-2
Online ISBN: 978-3-030-55130-8
eBook Packages: Computer ScienceComputer Science (R0)