Deep learning-based question answering system for intelligent humanoid robot

Journal of Big Data

Vincent Andreas¹  Widodo Budiharto¹  Alexander Agung Santoso Gunawan² 

[1] Computer Science Department, School of Computer Science, Bina Nusantara University, 11480, Jakarta, Indonesia;Mathematics Department, School of Computer Science, Bina Nusantara University, 11480, Jakarta, Indonesia;

关键词: Humanoid Robot;    NLP;    Big data;    Deep learning;   

DOI  :  10.1186/s40537-020-00341-6

来源: Springer

【 摘 要 】

BackgroundThe development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action.FindingsThe question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF).ConclusionsOur evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.

【 授权许可】

CC BY