Open Access
| Issue |
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
|
|
|---|---|---|
| Article Number | 01049 | |
| Number of page(s) | 13 | |
| DOI | https://doi.org/10.1051/epjconf/202534101049 | |
| Published online | 20 November 2025 | |
- G. Caldarini, S. Jaf, and K. McGarry, "A Literature Survey of Recent Advances in Chatbots," Information, vol. 13, no. 1, pp. 1-41, 2022. [Google Scholar]
- T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi et al., "Transformers: State-of-the-Art Natural Language Processing," in Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP), pp. 38-45, 2021. [Google Scholar]
- J. Summaira, M. Zafar, and M. Zulfiqar, "Recent Advances and Trends in Multimodal Deep Learning: A Review," arXiv preprint, arXiv:2105.11087, 2021. [Google Scholar]
- A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal et al., "Learning Transferable Visual Models From Natural Language Supervision," in Proc. Int. Conf. on Machine Learning (ICML), pp. 8748-8763, 2021. [Google Scholar]
- J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proc. NAACL-HLT, pp. 4171-4186, 2019. [Google Scholar]
- Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen et al., "RoBERTa: A Robustly Optimized BERT Pretraining Approach," arXiv preprint, arXiv:1907.11692, 2019. [Google Scholar]
- A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzman et al., "Unsupervised Cross-lingual Representation Learning at Scale," in Proc. ACL, pp. 8440-8451, 2020. [Google Scholar]
- M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in Proc. Int. Conf. Machine Learning (ICML), pp. 6105-6114, 2019. [Google Scholar]
- A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner et al., "An Image is Worth 16*16 Words: Transformers for Image Recognition at Scale," in Proc. Int. Conf. Learning Representations (ICLR), 2021. [Google Scholar]
- A. Baevski, H. Zhou, A. Mohamed, and M. Auli, "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 12449-12460, 2020. [Google Scholar]
- Y. Zhou, L. Chen, and J. Wang, "Deep Learning Approaches for Multimodal Fusion: A Review," IEEE Access, vol. 11, pp. 105422-105438, 2023. [Google Scholar]
- C. Jia, Y. Yang, Y. Xia, Y. Chen, Z. Parekh, H. Pham et al., "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision," in Proc. Int. Conf. Machine Learning (ICML), pp. 4904-4916, 2021. [Google Scholar]
- S. Wang, Z. Chen, and R. Xu, "Knowledge-Enhanced Conversational AI: A Survey of Recent Progress," Information Fusion, vol. 97, 2024. [Google Scholar]
- G. Caldarini, S. Jaf, and K. McGarry, "A Literature Survey of Recent Advances in Chatbots," Information, vol. 13, no. 1, pp. 1-41, 2022. [Google Scholar]
- T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal et al., "Language Models Are Few-Shot Learners," in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877-1901, 2020. [Google Scholar]
- S. S. Sonawane, S. Salgar, P. Nanagare, and A. Bhogaonkar, "A Survey on Multimedia Chatbot – A New Gen AI Chatbot," Int. J. Res. Publ. Rev., vol. 5, no. 1, pp. 6056-6059, 2024. [Google Scholar]
- J. J. Bird, "Improving Customer Service Chatbots with Attention-Based Transfer Learning," arXiv preprint, arXiv:2111.14621, 2022. [Google Scholar]
- A. Ranieri, I. Di Bernardo, and C. Mele, "Serving Customers Through Chatbots: Positive and Negative Effects on Customer Experience," J. Service Theory and Practice, vol. 34, no. 2, pp. 191-215, 2024. [Google Scholar]
- M. D. R. Haque and S. Rubya, "An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews," JMIR mHealth and uHealth, vol. 11, e44838, 2023. [CrossRef] [Google Scholar]
- S. Wang, Z. Chen, and R. Xu, "Knowledge-Enhanced Conversational AI: A Survey of Recent Progress," Information Fusion, vol. 97, 2024. [Google Scholar]
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