Enhancing Natural Language Processing in Virtual Assistants through Transformer Models
Description
This study aims to investigate the possibility of improving Natural Language Processing (NLP) skills in virtual assistants by using Transformer models. Virtual assistants are becoming more common in various fields, including smart homes, IoT voice interactions, mobile data mining, and healthcare (Kang et al., 2020). However, existing NLP techniques employed in virtual assistants frequently need help interpreting user goals and fully handling complicated inquiries. Vaswani et al. (2017) unveiled the Transformer model that changed NLP tasks with its attention mechanism and parallel processing capabilities. This study looks at how adopting Transformer-based methodologies might improve the performance, efficiency, and user experience of virtual assistants in various applications.
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Research Question
How might the use of Transformer models improve Natural Language Processing in virtual assistants, and what are the possible applications and advantages of such an approach in diverse domains?
The research will begin by reviewing the current literature on NLP approaches used in virtual assistants, emphasizing the combined tasks of intent detection and slot filling, as emphasized in the paper by Ni et al. (2020). The survey done by Wang et al. (2021) on incorporating human intelligence into NLP loops will also be studied to grasp the more significant implications of NLP improvements. Furthermore, as recognized by Padmanaban et al. (2023), it is critical to investigate the issues encountered in human-computer interfaces for mobile data mining, as well as the developing applications of text analytics and NLP in healthcare, as addressed by Eisenstein (2019).
Furthermore, the research will look at the construction of ideal search engines combining text summarization techniques and artificial intelligence, as described by Sekaran et al. (2020), as well as Bharatiya’s (2023) complete assessment of deep learning approaches for NLP. The research will also investigate the function of artificial intelligence in enhancing human-computer interactions, particularly in computer systems, as written by Chowdhary & Chowdhary (2020), as well as the special issue on AI in Human-Computer Interaction given by Antona et al. (2023). Finally, as done by Bouraoui et al. (2022), a complete overview of deep learning approaches for NLP, including their applications, will be reviewed, as will the basics of deep learning in NLP, as given by Yang et al. (2019).
References
Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023). Special issue on AI in HCI. International Journal of Human-Computer Interaction, 39(9), 1723-1726. https://doi.org/10.1080/10447318.2023.2177421
Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66. https://doi.org/10.47672/ejt.1473
Bouraoui, A., Jamoussi, S., & Hamadou, A. B. (2022). A comprehensive review of deep learning for natural language processing. International Journal of Data Mining, Modelling and Management, 14(2), 149-182. https://doi.org/10.1504/IJDMMM.2022.123356
Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. https://doi.org/10.1007/978-81-322-3972-7_19
Eisenstein, J. (2019). Introduction to natural language processing. MIT Press. https://books.google.com/books?hl=en&lr=&id=64hyEAAAQBAJ&oi=fnd&pg=PP1&dq=Natural+Language+Processing+book&ots=t7M5EMsdcF&sig=Ee6rs5lj803JwJGUm4eend2JE2w
Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172. https://doi.org/10.1080/23270012.2020.1756939
Ni, P., Li, Y., Li, G., & Chang, V. (2020). Natural language understanding approaches are based on the joint task of intent detection and slot filling for IoT voice interaction. Neural Computing and Applications, 32, 16149-16166. https://doi.org/10.1007/s00521-020-04805-x
Padmanaban, K., Sathiya, A., Jeevitha, K., & Rosaline, R. A. A. (2023, June). Human-computer interface challenges in mobile data mining. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 1661-1666). IEEE. https://doi.org/10.1109/ICSCSS57650.2023.10169161
Sekaran, K., Chandana, P., Jeny, J. R. V., Meqdad, M. N., & Kadry, S. (2020). Design of optimal search engine using text summarization through artificial intelligence techniques. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(3), 1268-1274. http://doi.org/10.12928/telkomnika.v18i3.14028
Wang, Z. J., Choi, D., Xu, S., & Yang, D. (2021). Putting humans in the natural language processing loop: A survey. arXiv preprint arXiv:2103.04044. https://doi.org/10.48550/arXiv.2103.04044
Yang, H., Luo, L., Chueng, L. P., Ling, D., & Chin, F. (2019). Deep learning and its applications to natural language processing. Deep learning: Fundamentals, theory and applications, 89-109. https://doi.org/10.1007/978-3-030-06073-2_4
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Question 
Reference List Assignment
Assessment Description
Select a topic of your choice to research. For your research, you must use Google Scholar or another reputable site. Note that this assignment is not an essay or a research paper. You are to:
1. Start with a title page, including the topic you selected.
Enhancing Natural Language Processing in Virtual Assistants through Transformer Models
2. Write one or two paragraphs that describe the topic (The description is succinct and clearly explains the topic.), followed by your research question (The research question is well worded and appropriate for the topic description).
Create a reference list with 10 references you may use should you decide to research the topic: all less than 5 years old. References should be from appropriate sources including at least 1 book and 5 journals. All references include the permalink or persistent link. Make sure that all sources are authoritative. You can include the following types of references:
1. Book
2. Journal articles
3. Website
4. Dissertation/thesis from a database
5. Streaming video
6. Book chapter