[1] A. Chowanda, A.D. Chowanda, Recurrent neural network to deep learn conversation in indonesian, Procedia computer science, 116 (2017) 579-586.
[2] K. Karpagam, K. Madusudanan, A. Saradha, Deep learning approaches for answer selection in question answering system for conversation agents, ICTACT Journal on Soft Computing, 10(2) (2020) 2040-2044.
[3] R. Yan, Y. Song, H. Wu, Learning to respond with deep neural networks for retrieval-based human-computer conversation system, in: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016, pp. 55-64.
[4] A. Engineered, Ameca The future face of robotics, in, Engineered Arts, (2022).
[5] Y. Sharma, S. Gupta, Deep learning approaches for question answering system, Procedia computer science, 132 (2018) 785-794.
[6] M. Tan, C. Dos Santos, B. Xiang, B. Zhou, Improved representation learning for question answer matching, in: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 464-473.
[7] C. Xiong, S. Merity, R. Socher, Dynamic memory networks for visual and textual question answering, in: International conference on machine learning, PMLR, 2016, pp. 2397-2406.
[8] T. Minato, K. Sakai, T. Uchida, H. Ishiguro, A study of interactive robot architecture through the practical implementation of conversational android, Frontiers in Robotics and AI, 9, (2022)
[9] A. Conneau, A. Baevski, R. Collobert, A. Mohamed, M. Auli, Unsupervised cross-lingual representation learning for speech recognition, arXiv preprint arXiv:2006.13979, (2020)
[10] S.-H. Lee, Y.-E. Lee, S.-W. Lee, Toward imagined speech based smart communication system: potential applications on metaverse conditions, in: 2022 10th International Winter Conference on Brain-Computer Interface (BCI), IEEE, 2022, pp. 1-4.
[11] P. Chang, S. Liu, K.D. Campbell, Robot Sound Interpretation: Learning Visual-Audio Representations for Voice-Controlled Robots, CoRR, (2021)
[12] S. Hamed, puppeteer robot: Designing a construction of a new interactive game based on imitative learning of a humanoid robot from a human, in: The first national conference of computer games; Opportunities and challenges, 1394 (in Persian).
[13] fastText, fastText: Library for efficient text classification and representation learning, in, Facebook.
[14] H. Hemati, fastText Word Embedding: The Persian Approach to Text Embedding, (2018)
[15] H. Hemati, fastText-Persian, in, Github, 2019.
[16] S. Ayobi, PersianQA, in, Github, 2021.
[17] A. Akhavan, Text Classification Emojify, in, Github, 1399.
[18] J.C. Vasquez-Correa, J.C. Guerrero-Sierra, J.L. Pemberty-Tamayo, J.E. Jaramillo, A.F. Tejada-Castro, One system to rule them all: A universal intent recognition system for customer service chatbots, arXiv preprint arXiv:2112.08261, (2021)
[19] J.J. Bird, A. Ekárt, D.R. Faria, Chatbot Interaction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classification, Journal of Ambient Intelligence and Humanized Computing, 14(4) (2023) 3129-3144.
[20] E.H. Almansor, F.K. Hussain, O.K. Hussain, Supervised ensemble sentiment-based framework to measure chatbot quality of services, Computing, 103 (2021) 491-507.
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