|
|
Sentiment Analysis for Intelligent Customer Service Chatbots |
SONG Shuangyong, WANG Chao, CHEN Chenglong, ZHOU Wei, CHEN Haiqing |
Intelligent Innovation Center, Alibaba Group, Hangzhou, Zhejiang 311121, China |
|
|
Abstract AliMe is a recently developed chatbot, focused on intelligent customer service domain. The emotion analysis technologies have been successfully utilized in many modules of AliMe. The technical details of those emotion analysis based modules are presented, including user emotion detection, user emotion comfort, emotional generative chatting, customer service quality control, session satisfaction prediction and intelligent entrance for manual customer service. Furthermore, some user interface examples of those emotional modules are also introduced to improve understanding of their effects.
|
Received: 30 May 2019
|
|
|
|
|
[1] Okuda T,Shoda S. AI-based chatbot service for financial industry [J]. Fujitsu Scientific and Technical Journal,2018,54(2):4-8.
[2] Zhu X. Case II (Part A):JIMI’s growth path:Artificial intelligence has redefined the customer service of JD.com [C]//Proceedings of the Emerging Champions in the Digital Economy. Springer,Singapore,2019:91-103.
[3] Li F-L,Qiu M,Chen H,et al. AliMe assist:An intelligent assistant for creating an innovative e-commerce experience [C]//Proceedings of the ACM Conference on Information and Knowledge Management,2017:2495-2498.
[4] Shen D,Wang G,Wang W,et al. Baseline needs more love:On simple word-embedding-based models and associated pooling mechanisms [C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics,2018:440-450.
[5] Zeng D,Liu K,Chen Y,et al.Distant supervision for relation extraction via piecewise convolutional neural networks [C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing,2015:1753-1762.
[6] Wang G,Li C,Wang W,et al. Joint embedding of words and labels for text classification [C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics,2018:2321-2331.
[7] Wang Y Huang,M Zhao L. Attention-based LSTM for aspect-level sentiment classification [C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing,2016:606-615.
[8] 庞亮,兰艳艳,徐君,等. 深度文本匹配综述[J]. 计算机学报,2017,40(4):985-1003.
[9] Yin W,Schütze H,Xiang B,et al. Abcnn:Attention-based convolutional neural network for modeling sentence pairs [J]. Transactions of the Association for Computational Linguistics,2016(4):259-272.
[10] Hu B,Lu Z,Li H,et al. Convolutional neural network architectures for matching natural language sentences [C]//Proceedings of Advances in Neural Information Processing Systems,2014:2042-2050.
[11] Qiu X,Huang X. Convolutional neural tensor network architecture for community-based question answering [C]//Proceedings of the 24th International Joint Conference on Artificial Intelligence,2015:1305-1311.
[12] Pang L,Lan Y,Guo J,et al. Text matching as image recognition [C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence,2016:2793-2799.
[13] Lu Z,Li H. A deep architecture for matching short texts [C]//Proceedings of Advances in Neural Information Processing Systems,2013:1367-1375.
[14] McCandless M,Hatcher E,Gospodnetic O. Lucene in action:Covers Apache Lucene 3.0 [M]. Manning Publications Co,2010.
[15] Yu J,Qiu M,Jiang J,et al. Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce [C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining,2018:682-690.
[16] Zhou L,Gao J,Li D,et al. The design and implementation of XiaoIce:An Empathetic Social Chatbot [R].arXiv preprint arXiv:1812. 08989,2018.
[17] Zhou H,Huang M,Zhang T,et al. Emotional chatting machine:Emotional conversation generation with internal and external memory [C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence,2018:730-738.
[18] Li J,Galley M,Brockett C,et al. A persona-based neural conversation model [C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics,2016:994-1003.
[19] Young T,Cambria E,Chaturvedi I,et al. Augmenting end-to-end dialogue systems with commonsense knowledge [C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence,2018:4970-4977.
[20] Bahdanau D,Cho K,Bengio Y. Neural machine translation by jointly learning to align and translate [C]//Proceedings of the 3rd International Conference on Learning Representations,2015.
[21] Zaremba W,Sutskever I,Vinyals O. Recurrent neural network regularization [C]//Proceedings of the 3rd International Conference on Learning Representations,2015.
[22] Song S,Meng Y,Shi Z,et al. A simple yet effective method for summarizing microblogging users with their representative tweets [C]//Proceedings of the 2017 International Conference on Asian Language Processing,2017:310-313.
[23] Guo H,Tang R,Yey Y,et al. DeepFM:A Factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence,2017:1725-1731.
[24] Chen C. tensorflow-XNN [EB]. 2018. https://git hub.com/ChenglongChen/tensorflow-XNN.
|
|
|
|