Deep learning-based question answering system for intelligent humanoid robot
Abstract Background The 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 pe...
| Asıl Yazarlar: | , , |
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| Materyal Türü: | Makale |
| Dil: | English |
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SpringerOpen
2020-09-01
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| Seri Bilgileri: | Journal of Big Data |
| Konular: | |
| Online Erişim: | http://link.springer.com/article/10.1186/s40537-020-00341-6 |
| _version_ | 1828402603081334784 |
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| author | Widodo Budiharto Vincent Andreas Alexander Agung Santoso Gunawan |
| author_facet | Widodo Budiharto Vincent Andreas Alexander Agung Santoso Gunawan |
| author_sort | Widodo Budiharto |
| collection | DOAJ |
| description | Abstract Background The 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. Findings The 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). Conclusions Our 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. |
| first_indexed | 2024-12-10T10:03:37Z |
| format | Article |
| id | doaj.art-d6d059d804514105b812f05d3f9d5fc8 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-10T10:03:37Z |
| publishDate | 2020-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-d6d059d804514105b812f05d3f9d5fc82022-12-22T01:53:17ZengSpringerOpenJournal of Big Data2196-11152020-09-017111010.1186/s40537-020-00341-6Deep learning-based question answering system for intelligent humanoid robotWidodo Budiharto0Vincent Andreas1Alexander Agung Santoso Gunawan2Computer Science Department, School of Computer Science, Bina Nusantara UniversityComputer Science Department, School of Computer Science, Bina Nusantara UniversityMathematics Department, School of Computer Science, Bina Nusantara UniversityAbstract Background The 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. Findings The 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). Conclusions Our 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.http://link.springer.com/article/10.1186/s40537-020-00341-6Humanoid RobotNLPBig dataDeep learning |
| spellingShingle | Widodo Budiharto Vincent Andreas Alexander Agung Santoso Gunawan Deep learning-based question answering system for intelligent humanoid robot Journal of Big Data Humanoid Robot NLP Big data Deep learning |
| title | Deep learning-based question answering system for intelligent humanoid robot |
| title_full | Deep learning-based question answering system for intelligent humanoid robot |
| title_fullStr | Deep learning-based question answering system for intelligent humanoid robot |
| title_full_unstemmed | Deep learning-based question answering system for intelligent humanoid robot |
| title_short | Deep learning-based question answering system for intelligent humanoid robot |
| title_sort | deep learning based question answering system for intelligent humanoid robot |
| topic | Humanoid Robot NLP Big data Deep learning |
| url | http://link.springer.com/article/10.1186/s40537-020-00341-6 |
| work_keys_str_mv | AT widodobudiharto deeplearningbasedquestionansweringsystemforintelligenthumanoidrobot AT vincentandreas deeplearningbasedquestionansweringsystemforintelligenthumanoidrobot AT alexanderagungsantosogunawan deeplearningbasedquestionansweringsystemforintelligenthumanoidrobot |