International Journal of Intelligent Systems · 2023 · 12 citations · 29 references
ComputingComputer ScienceRobotics EngineeringArtificial IntelligenceHuman-computer InteractionRoboticsAffective ComputingConversational InterfacesConversational User InterfacesDialogue SystemsHuman-machine InterfacesMultimodal InteractionConversational AgentsDialog SystemsDialogue Response GenerationEmotionsEvaluation Of Dialogue SystemsMultimodal ConversationMultimodal Dialogue SystemsPrototype Evaluation
Intelligent service robots have become an indispensable aspect of modern‐day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human‐machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human‐machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human‐like dialogue experience.
29
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · 76.3K citations · Full text
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su et al. · International Journal of Computer Vision · 2015 · 40.2K citations
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, Quoc V. Le · arXiv (Cornell University) · 2014 · 13.4K citations · Full text
Computing, Computer Science, French +14
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie, Ross Girshick, Piotr Dollár et al. · 2017 · 11.9K citations