深度编码网络下的英语点餐机器人交互系统设计

目的：研究点餐机器人情感交互的设计思路与理念，设计以人为本的智能情感交互方法点餐机器人。方法：采用BLSTM网络构建英语语义参量的编码网络，进而提出主旨型注意力模式，该模式可通过赋权值的方式提取相应数据，然后设计约束型SeqGAN网络架构完成解码，从而调整生成装置参量，缩小生成点餐语言与真人英语情感交互回复间的差距。结果：与Du-Model法和HRED-Model法相比，BLSTM-SeqGAN法的困惑指标更小且精准度更高，并随迭代数目增加而稳定程度更高。结论：该方法能够获得更加自然、真实与友好的情感交互反应。

Interactive system design of English ordering robot based on deep coding network

Objective: This paper studies the design ideas and concepts of the emotional interaction of the ordering robot, so as to design the human-based intelligent emotional interaction method of the ordering robot. Methods: Firstly, BLSTM network was used to construct the English semantic parameter coding network, then put forward thrust type attention model, this model can extract data by means of weighting, after that, the constrained SeqGAN network architecture is designed to complete the decoding, so as to adjust the parameters of the generating device and narrow the gap between the generated ordering language and human English emotional interaction response. Results: Compared with Du-Model method and HRED-Model method, the BLSTM-SeqGAN method has smaller confusion index and higher accuracy, and becomes more stable as the number of iterations increases. Conclusion: This method can obtain more natural, real and friendly emotional interaction response.